Katholieke Universiteit Leuven
Faculteit Bio-ingenieurswetenschappen
DISSERTATIONES DE AGRICULTURA
Doctoraatsproefschrift nr. 789 aan de Faculteit
Bio-ingenieurswetenschappen van de K.U.Leuven
HIGH THROUGHPUT MEASUREMENT OFTASTE COMPONENTS OF FRUIT JUICES
Promotoren:
Prof. J. Lammertyn, K.U.Leuven
Prof. B. Nicolaı, K.U.Leuven
Leden van de jury:
Prof. E. Decuypere, voorzitter,
K.U.Leuven
Prof. W. Keulemans, K.U.Leuven
Prof. A. Legin, Saint-Petersburg
University, Russia
Prof. R. Schoonheydt, K.U.Leuven
Prof. E. Schrevens, K.U.Leuven
Dr. Ir. B. Verlinden, VCBT vzw
.
Proefschrift voorgedragen tot
het behalen van de graad van
Doctor in de
Bio-ingenieurswetenschappen
door
Katrien BEULLENS
APRIL 2008
ii
Voorwoord
Bladerend door de proefdrukken lijkt dit doctoraat op een reisdocument.
Figuurlijk, omdat de verschillende experimenten vertrek- en aankomstpunten
zijn geweest tijdens de afgelopen vier jaar onderzoek. Letterlijk, omdat
de metingen in Rusland, de Verenigde Staten en Belgie uitgevoerd werden
in samenwerking met kleurrijke persoonlijkheden van verschillende natio-
naliteiten. Dit alles maakte het een ontzettend boeiende en unieke reis.
Zonder enkele bijzondere mensen had ik mijn eindbestemming nooit bereikt.
Daarom een woordje van dank.
Mijn dank gaat allereerst uit naar mijn promotoren, Prof. Bart Nicolaı
en Prof. Jeroen Lammertyn. Bart, dank zij jou kreeg ik het ticket om
mijn reis aan te vatten. Het was een plezier om deel uit te maken van
jouw groep. Je hebt me steeds de mogelijkheid gegeven om verder te gaan.
Jeroen, jij hebt me enorm geholpen met het uitspitten van de reisgidsen in de
wereld van FTIR en multivariate statistiek. Bedankt voor het vertrouwen,
de bemoedigingen en de nauwgezette opvolging van mijn werk.
My gratitude also goes to Prof. Andrey Legin. Andrey, thanks for your
time and interest in my work and for giving me the chance to experience
the coldest month of my life at Saint-Petersburg University. I would also
like to thank Dima and Alisa from the Laboratory of Chemical Sensors of
Saint-Petersburg University for their cooperation during my stay at their
’electronic tongue lab’.
Verder wens ik Prof. Decuypere, voorzitter van de jury, en Prof. Keule-
mans, Prof. Legin, Prof. Schrevens, Prof. Schoonheydt en Dr. Ir. Verlin-
den, leden van de jury, te bedanken voor het kritisch bestuderen van mijn
doctoraatstekst.
iii
iv
I would like to thank Prof. Irudayaraj for giving me the opportunity to
take my first steps in the field of FTIR spectroscopy in his lab at Penn State
College. Prof. Schoonheydt wil ik speciaal bedanken voor het openstellen
van zijn laboratorium voor de FTIR experimenten die hierop volgden.
Muchas gracias a Ruby Raquel Ormeno Ponce de Leon por ajudarme
con el trabajo del infra rojo. Disfrute mucho el ano que trabajamos juntos
en tu tesina.
I also want to thank Peter Meszaros for his help with the development of
the SIA-ATR-FTIR system and for giving us the opportunity to work with
the ASTREE ET.
Bedankt Elfie en Bert, voor de vele praktische hulp die ik van jullie
kreeg. Dank zij jullie werden duizenden tomaten gemixt en gecrusht in een
oogwenk. Samenwerken met jullie was een leuk avontuur.
Dit doctoraatswerk was niet mogelijk geweest zonder de financiele steun
van het IWT-Vlaanderen en een bilaterale samenwerking tussen de Univer-
siteit van Sint-Petersburg en de K.U. Leuven. Ik wens de leden van de
gebruikerscommissie van het CLO/IWT/040726 project te bedanken voor
hun opbouwende kritiek.
Tijdens mijn reis ben ik veel leuke mensen tegengekomen. Bedankt
aan al mijn huidige en ex-collegas van MeBioS en het VCBT: Amalia,
Andrew, Anh, Annemie, Ann, Ayenew, Benny, BertV, BertVdb, Bram,
Carine, Christine, Dinh, Elfie, ElsB, ElsVan, ElsVer, Erika, Evelien, Evgeny,
Fernando, Filip, Fumihiko, Hibru, Inge, Janina, Javier, JeroenP, JeroenT,
Josee, Jurgen, Justyna, Kate, Katrijn, Kris, Maarten, Mailin, Mari Carmen,
Michele, Mulugeta, Nahor, Nhi, Nicolas, Nico, Pablo, Pal, Pathompong,
Patrick, Perla, Peter, Pieter, Romina, Sabina, Sabine, Sofie, Steven, StijnS,
StijnVer, Tongchai, Tri, Tuan, Uzuki, Victor, Violetta, Wendy and Yegermal.
Thanks for your help, the lunches at ViaVia and the pleasant times and
laughther inside and outside the lab.
Special thanks go to two very dear friends I made during my stay at the
lab: Amalia and Mari Carmen. Prequitas, muchas gracias por las risas y
las discusiones mas y (en general) menos cientıficas. Echo de menas vuestra
presencia en Belgica. Espero que podemos estar juntos en algun lugar del
mundo en algun momento de nuestra vida!
Voorwoord v
Ook een woordje van dank aan mijn vrienden die niks met MeBioS te
maken hebben. Iedereen van de scouts, de Spaanse bende, mijn assistenten-
fractiegenoten en al mijn andere vrienden, bedankt voor de vele momenten
van ontspanning en plezier die mijn reis vaak wat luchtiger maakten. Ook
voor jullie is dit het einde van een tijdperk: het is gedaan met de gratis
tomaten, appels en bananen...
Tenslotte, wil ik mijn ouders, broer Geert en schoonzus Nynke bedanken
voor hun betrokkenheid, vertrouwen en eeuwige steun tijdens de grote avon-
turen in mijn leven en de leuke momenten samen met Jelle en Wybe. Zonder
de mogelijkheden die jullie me gegeven hebben, had ik hier nooit gestaan!
Katrien,
April 2008
vi
Nederlandse samenvatting
Traditioneel wordt gebruik gemaakt van sensorische proefpanels en instru-
mentele technieken om de smaak van voedingsproducten te bepalen. Sen-
sorische panels geven het meest realistische beeld van de smaak zoals die er-
varen wordt door de mens. Er zijn echter belangrijke nadelen verbonden aan
deze meettechniek, zoals de herhaalbaarheid, hoge kost en verzadiging van
de panelleden. Hogedrukvloeistofchromatografie (HPLC) en andere instru-
mentele technieken geven informatie over de chemische samenstelling van
een product en beschrijven zo het smaakprofiel van dit product. Deze tradi-
tionele technieken vereisen echter een uitvoerige en tijdsrovende staalvoor-
bereiding en getraind personeel om de toestellen te bedienen. In de voedings-
sector is er daarom een nood aan objectieve en eenvoudige hoge doorvoer-
meettechnieken voor smaakbepaling ter aanvulling van de sensorische panels.
In dit werk worden de mogelijkheden van twee snelle en objectieve meettech-
nieken, de elektronische tong (ET) en geattenueerde totale reflectie-Fourier
getransformeerde infrarood (ATR-FTIR) spectroscopie, voor smaakprofile-
ring van groenten en fruit geevalueerd.
In een eerste deel werd de performantie van de ET ontwikkeld aan de
Universiteit van Sint-Petersburg (ETSPU) vergeleken met de commercieel
beschikbare ASTREE ET (Alpha M.O.S., Toulouse, Frankrijk). Ondanks
de grote verschillen in het meetprotocol van beide systemen, toonden beide
ET’en de mogelijkheid om tomatenrassen te klassificeren op basis van hun
suiker-, zuur- en mineraalgehalte. De ETSPU kan ook de concentratie van
individuele componenten in een tomatenmatrix meten, terwijl de ASTREE
ET hier niet toe in staat is. De selectie van de sensoren blijkt echter cruciaal
om goede meetresultaten te verkrijgen.
vii
viii
Het potentieel van ATR-FTIR voor de klassificatie van rassen en de
kwantificatie van smaakcomponenten werd bestudeerd in verscheidene ex-
perimenten. De specifieke vibraties van chemische bindingen maakten het
mogelijk om smaakcomponenten te relateren aan de absorptiespectra. Eerst
werden de karakteristieke absorpties van IR licht door een reeks smaakbe-
palende componenten bepaald in pure en mengselvorm. Vervolgens toonde
ATR-FTIR aan een onderscheid te kunnen maken tussen vruchten op basis
van hun chemische samenstelling. Het systeem bewees nauwkeurige voor-
spellingen te kunnen maken indien de concentratieverschillen van de opgeme-
ten componenten groot zijn.
De mogelijkheden van de ETSPU en ATR-FTIR in kwaliteitscontrole
werden geevalueerd in een experiment waarin multivruchtensappen en de
individuele siropen waaruit ze bestaan werden gegroepeerd. Beide tech-
nieken slaagden erin de stalen succesvol te klassificeren en kleine hoeveelhe-
den siroop te kwantificeren in de multivruchtensappen. De ETSPU en ATR-
FTIR toonden hiermee aan geschikte technieken te zijn voor kwaliteitscon-
trole dank zij hun goede resultaten, gebruiksgemak en meetsnelheid.
In een volgende fase werd de ATR-FTIR opstelling uitgebreid met een
sequentieel injectie (SIA) systeem voor de automatisatie van de metingen. In
een eerste stap werd het doorstroomsysteem gebouwd en geoptimaliseerd op
basis van modeloplossingen. Vervolgens werd het SIA-ATR-FTIR systeem
gebruikt voor de groepering van tomatenrassen op basis van hun absorptie
spectrum en de predictie van hun belangrijkste smaakcomponenten. De
voordelen van SIA-ATR-FTIR, de snelle analyses en veelzijdigheid van de
eenvoudige instrumentatie, werden hierbij duidelijk.
Tenslotte werden de ET’en en SIA-ATR-FTIR toegepast om de smaak
van vruchten te bepalen zoals die gescoord wordt door een getraind sen-
sorisch panel. De ETSPU en ASTREE ET waren in staat om de zuurheid,
het zoutgehalte en het umami gehalte van een tomaat te voorspellen in enkele
minuten tijd. De predictie van zoetheid met de ETSPU vereist de selectie
van specifieke sensoren. SIA-ATR-FTIR kan nauwkeurige voorspellingen
maken van alle bestudeerde smaken. Zowel de ET als ATR-FTIR toonden
aan potentieel te hebben in vele toepassingen in de voedingsindustrie.
Abstract
Sensory and instrumental techniques are traditionally used to determine the
taste of food products. Sensory panels give by far the most realistic image
of the taste of a product as perceived by humans, however, they have se-
rious drawbacks such as reproducibility, high cost and taste saturation of
the panelist. High pressure liquid chromatography (HPLC) and other in-
strumental techniques give information on the chemical composition of the
sample and, hence, are useful to describe the taste profile of the product.
These traditional techniques often require a laborious and time-consuming
sample preparation and skilled people to operate the equipment. In food
research there is a need for objective high throughput taste profiling to
complement sensory panels. In this thesis, the potential of two rapid and
objective measurement techniques, the electronic tongue (ET) and attenu-
ated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR),
for taste profiling of fruit is evaluated.
The possibilities of the ET developed at the University of Saint-Petersburg
(ETSPU) were compared to the commercially available ASTREE ET devel-
oped by Alpha M.O.S. (Toulouse, France). Considerable differences in the
measurement protocol of both systems were listed. Despite the differences,
both ET’s did show the possibility to classify tomato cultivars based on
their sugar, acid and mineral content. The ETSPU can predict individual
compounds in a tomato matrix, while the ASTREE ET cannot quantify
the concentration of any of the studied compounds. A proper selection of
sensors, however, is crucial to reach reliable and repeatable results with the
ETSPU.
ATR-FTIR allows relating taste compounds directly to the absorbance
ix
x
spectra through the study of specific vibrations of chemical bonds. The po-
tential to use this technique for the classification of cultivars and the quan-
tification of taste compounds was studied in several experiments. First, pure
compounds and mixtures were analyzed to determine the important absorp-
tion bands of IR light which are characteristic for each taste compound.
Second, real fruit samples were analyzed. ATR-FTIR showed to be able
to distinguish between fruit samples based on their chemical composition.
The system also proved to be very accurate in the quantification of taste
compounds when the concentration ranges are large and the influence of the
matrix is low.
Next, the potential of the ETSPU and ATR-FTIR as tools for rapid qual-
ity control was studied. Using these systems it is possible to group multifruit
juices and the individual syrups they are made of. Both rapid techniques
made it possible to predict low concentrations of syrup in the complex multi-
fruit juice. The ETSPU and ATR-FTIR showed to be promising techniques
for quality control because of their good performance, easy use and detection
speed.
In a next phase, the ATR-FTIR system was extended with a sequen-
tial injection analysis (SIA) set-up. In a first step, the development and
optimization of the SIA-ATR-FTIR were studied into detail using model so-
lutions. Subsequently, the system was used to discriminate between tomato
cultivars based on their specific absorption of sugars and acids and to de-
termine the concentrations of the most important taste compounds. The
advantages of SIA-ATR-FTIR include rapid analysis, versatility and sim-
plicity of the flow injection instrumentation.
Finally, the potential of the ET and SIA-ATR-FTIR to determine the
taste of fruit as measured by a trained sensory panel was examined. Using
the ETSPU and the ASTREE ET sourness, saltiness and umami could be
predicted accurately. The prediction of sweetness required the selection of
specific sensors for the ETSPU. SIA-ATR-FTIR was able to make accurate
predictions on sweetness, sourness, saltiness and umami. Both the ET’s
and ATR-FTIR showed to have a potential for many applications in food
industry.
Contents
Voorwoord iii
Nederlandse samenvatting vii
Abstract ix
Contents xi
Symbols and Abbreviations xv
1 General introduction 1
1.1 Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Taste . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Measurement of taste . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Objectives and outline of the thesis . . . . . . . . . . . . . . . 6
2 Literature review 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Traditional techniques for the measurement of taste . . . . . 10
2.2.1 High performance liquid chromatography . . . . . . . 10
2.2.2 Enzymatic analysis . . . . . . . . . . . . . . . . . . . . 11
2.2.3 Atomic absorption spectroscopy . . . . . . . . . . . . . 12
2.2.4 Soluble solids content and titratable acidity . . . . . . 12
2.2.5 Sensory analysis . . . . . . . . . . . . . . . . . . . . . 12
2.3 Electronic tongue technology . . . . . . . . . . . . . . . . . . 13
2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 14
xi
xii CONTENTS
2.3.3 Instrumentation . . . . . . . . . . . . . . . . . . . . . 19
2.3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . 22
2.4 Fourier transform infrared spectroscopy . . . . . . . . . . . . 24
2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.3 Instrumentation . . . . . . . . . . . . . . . . . . . . . 29
2.4.4 Applications . . . . . . . . . . . . . . . . . . . . . . . 36
2.5 Multivariate analysis techniques . . . . . . . . . . . . . . . . . 37
2.5.1 Data pretreatment . . . . . . . . . . . . . . . . . . . . 38
2.5.2 Principal component analysis . . . . . . . . . . . . . . 39
2.5.3 Principal component regression . . . . . . . . . . . . . 40
2.5.4 Partial least squares . . . . . . . . . . . . . . . . . . . 40
3 Electronic tongue technology 45
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . 47
3.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.2 Electronic tongues . . . . . . . . . . . . . . . . . . . . 50
3.2.3 Reference techniques . . . . . . . . . . . . . . . . . . . 54
3.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . 56
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.1 Classification of apple and tomato cultivars and quan-
tification of taste compounds . . . . . . . . . . . . . . 58
3.3.2 Comparison of two electronic tongues . . . . . . . . . 70
3.3.3 Quality control of fruit juices . . . . . . . . . . . . . . 79
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.4.1 Classification of apple and tomato cultivars and quan-
tification of taste compounds . . . . . . . . . . . . . . 85
3.4.2 Comparison between two electronic tongues . . . . . . 87
3.4.3 Quality control of fruit juices . . . . . . . . . . . . . . 91
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4 Fourier transform infrared spectroscopy 95
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
CONTENTS xiii
4.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . 98
4.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.2.2 ATR-FTIR . . . . . . . . . . . . . . . . . . . . . . . . 102
4.2.3 Reference techniques . . . . . . . . . . . . . . . . . . . 105
4.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . 105
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.1 Taste compounds . . . . . . . . . . . . . . . . . . . . . 107
4.3.2 Classification of apple and tomato cultivars and quan-
tification of taste compounds . . . . . . . . . . . . . . 116
4.3.3 Extracted samples versus juices . . . . . . . . . . . . . 124
4.3.4 Dilutions and standard additions . . . . . . . . . . . . 133
4.3.5 Quality control of fruit juices . . . . . . . . . . . . . . 135
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.4.1 Classification of samples . . . . . . . . . . . . . . . . . 142
4.4.2 Quantification of taste compounds . . . . . . . . . . . 144
4.4.3 Quality control of fruit juices . . . . . . . . . . . . . . 146
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
5 Sequential injection ATR-FTIR 151
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
5.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . 153
5.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 153
5.2.2 Measurement techniques . . . . . . . . . . . . . . . . . 155
5.2.3 Optimization design . . . . . . . . . . . . . . . . . . . 156
5.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . 158
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
5.3.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . 159
5.3.2 Data exploration of tomato samples . . . . . . . . . . 162
5.3.3 Classification of tomato cultivars . . . . . . . . . . . . 164
5.3.4 Quantification of taste compounds . . . . . . . . . . . 167
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
5.4.1 Optimization . . . . . . . . . . . . . . . . . . . . . . . 168
5.4.2 Classification of tomato cultivars . . . . . . . . . . . . 169
5.4.3 Quantification of taste compounds . . . . . . . . . . . 170
xiv Table of Contents
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
6 Relation between sensory analysis and instrumental mea-
surements 173
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
6.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . 175
6.2.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . 175
6.2.2 Sensory panel analysis . . . . . . . . . . . . . . . . . . 177
6.2.3 Instrumental techniques . . . . . . . . . . . . . . . . . 178
6.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . 179
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
6.3.1 Addition of chemical taste compounds to a tomato juice180
6.3.2 Tomatoes with a wide range of tastes . . . . . . . . . 182
6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
6.4.1 Addition of chemical compounds to a tomato juice . . 190
6.4.2 Tomatoes with a wide range of tastes . . . . . . . . . 191
6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
7 General conclusions and future work 195
7.1 General conclusions . . . . . . . . . . . . . . . . . . . . . . . . 195
7.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Bibliography 201
List of publications 219
Symbols and Abbreviations
A absorbance units
ai activity of a primary ion
ao activity of oxidized species
ar activity of reduced species
AAS atomic absorption spectroscopy
ACLS augmented classical least squares
AMTIR amorphous material transmitting infrared light
ATR attenuated total reflectance
BB Box-Behnken design
BI bead injection
B(σ) imperfection of optical design
c chromophore concentration
ci predicted concentration
ci known concentration
CCD central composite design
CLS classical least squares
CV coefficient of variation
δ pathlenght difference
DA diode array
xv
xvi
ε molar extinction coefficient
Eo standard electrode potential
EHT enzymatic high throughput
EMSC extended multiple signal correction
ET electronic tongue
ETSPU electronic tongue developed at Saint-Petersburg University
F Faraday constant
FFT fast Fourier transform
FIA flow injection analysis
FTIR Fourier transform infrared spectroscopy
GC gas chromatography
GLM general linear model
HATR horizontal attenuated total reflectance
HPLC high performance liquid chromatography
I detected light intensity
I0 incident light intensity
IDS current between drain and source
IR infrared
ISFET ion-selective field effect transistors
Kij selective coefficient of the ion-selective membrane to the pri-
mary ion i in presence of an interfering ion j
l pathlength
λ wavelength of light
Symbols and Abbreviations xvii
LAPV large amplitude pulse voltametry
LC liquid chromatography
LOD limit of detection
LOV lab-on-valve
LV latent variable
mid-IR mid-infrared
MLR multiple linear regression
M.O.S. Multi Organoleptic Systems
MS mass spectrometry
MSC multiple scatter correction
n number of samples
N number of atoms in a molecule
n1 refractive index of the prism material
n2 refractive index of the medium
NIR near infrared
PC principal component
PCA principal component analysis
PCR principal component regression
P (δ) energy at detector
P (σ) incident energy at interferometer
PLS partial least squares
PLS-DA partial least squares-discriminant analysis
PVC polyvinylchloride
R correlation coefficient
R gas constant (8.314 Jmol−1K−1)
RI refractive index
xviii
RMSEC root mean square error of calibration
RMSECV root mean square error of cross validation
RPD ratio of prediction to deviation
σ wavenumber of light
SAPV small amplitude pulse voltametry
SAS Statistical Analysis System
SIA sequential injection analysis
SNV standard normal variate correction
SSC soluble solids content
S-SENCE Swedish Sensor Centre
St.Dev. standard deviation
T temperature
θc critical angle
θi angle of incident light
TA titratable acidity
UV ultraviolet
VCBT Flanders Centre of Postharvest Technology
zi electrical charge of a primary ion
zj electrical charge of an interfering ion
Chapter 1
General introduction
1.1 Quality
Fruit and vegetables are important consituents of the human diet. They
are sources of many essential vitamins and minerals, they are low in fat
and high in dietary fiber and complex carbohydrates. By a high intake of
fruit and vegetables human health will benefit and the immune system will
boost. Experts suggest that consumers should have five portions of fruit and
vegetables every day to improve general well-being. Production of fruit and
vegetables in Flanders was more than 2 million ton in 2005 (VBT, 2006).
The most important fruit in Belgium is apple, which acounts for 56% of the
total fruit production. Both the health benefit and the high production in
Belgium are motives for research concering fruit and vegetable quality.
The quality of fruit is generally determined by intrinsic properties of the
product and the appreciation of the consumer. Important attributes are
the nutritional quality determined by the energetic value, proteins, vitamins
and minerals; the convenience quality which is related to the storability and
the ease-to-handle the product; and the safety, which includes the absence
of fungi, mycotoxins and pesticides. The most important quality aspects are
safety, appearance, texture, aroma and taste. Both aroma and taste, which
are organoleptic quality attributes, are probably the most time consuming,
difficult and expensive properties to evaluate (Jongen, 2002). The focus of
the work presented in this thesis will be on the taste of fruit. The influence
1
2 1.2 Taste
of aroma and texture on the overall appreciation of a fruit will not be taken
into acount.
1.2 Taste
Taste is a very subjective quality characteristic of food products. It is a
fundamental chemical sense, like olfaction, which involves the detection of
stimuli dissolved in water, oil or saliva by the taste buds (Drewnowksi, 2001).
Humans can only taste differences in the concentration of substances, and
not absolute concentrations, and their sensitivity to levels that are lower
than those which appear in saliva is low. In addition to the concentration of
a taste stimulus, other conditions in the mouth that affect taste perception
are the temperature, viscosity, rate, duration and area of application of the
stimulus, the chemical state of the saliva and the presence of other tastants
in the solution being tasted (Meilgaard et al., 2007).
Taste is described by five gustatory perceptions, sweetness, sourness,
saltiness, umami and bitterness, caused by soluble substances in the mouth
(Meilgaard et al., 2007). The five tastes are mainly caused by the presence of
respectively sugars, organic acids, salts, monosodium-glutamate, phenolics
and alkaloids. The sensation of a taste can, however, not simply be explained
by the presence of a compound. Synergistic effects exist between different
compounds, so that the sensation of a taste cannot solely be explained by
the content of one group of compounds (Stevens et al., 1977; Salles et al.,
2003). As an example, the average composition of a tomato juice is shown
in Table 1.1.
Sweetness is mainly produced by the presence of sugars in a food product.
It is often connected to aldehydes and ketones, which contain a carbonyl
group. The average human detection threshold for sucrose in water is 10
mM. Fructose is 50% more sweet than sucrose, while glucose is 50% less sweet
(Breslin et al., 1994). Often, synergistic effects occur between sugars and
acids (Stevens et al., 1977; Fernandez-Ruiz et al., 2004). Also, the presence
of salts and some volatile compounds intensifies sweetness (De Bruyn et al.,
1971; Stevens et al., 1977; Noble, 1996; Stevenson et al., 1999).
Sourness is the taste that detects acidity and is, thus, triggered by H+
General introduction 3
ions and free acids. Malic acid is found to be up to 14% more sour than
citric acid. The average human detection threshold for HCl in water is 0.09
mM. The effect of sugars on the perception of sourness is much less intense
than the effect of acids on sweetness (Stevens et al., 1977; Baldwin et al.,
1998). The relation between pH, titratable acidity and total acids is not
clear since several authors give different statements on this subject (Petro-
Truza, 1987; Malundo et al., 1995; Fernandez-Ruiz et al., 2004). Several
volatile substances are found to have a correlation with sourness (Noble,
1996; Stevenson et al., 1999).
Table 1.1: Average composition of the dry matter content of tomato juice.
Constituent %
Fructose 25
Glucose 22
Sucrose 1
Citric acid 9
Malic acid 4
Protein 8
Dicarboxylic amino acids 2
Pectic substances 7
Cellulose 6
Hemicellulose 4
Minerals 8
Lipids 2
Ascorbic acid 0.5
Pigments 0.4
Other amino acids, vitamins 1
and polyphenols
Volatiles 0.1
Saltiness is a taste produced primarily by the presence of Na+ ions. NaCl
is the most known of all salts. It stimulates sweetness at low concentrations
and tastes somewhat sour at mid range concentrations. Other salts can
taste significantly sour or bitter in addition to salty. The average human
detection threshold for NaCl in water is 10 mM. After the taste buds have
4 1.2 Taste
adapted to NaCl, the sourness and bitterness of many salts increase (Smith
and van der Klaauw, 1995).
Bitter taste is often perceived to be unpleasant, sharp or disagreeable.
Since numerous harmful compounds, including secondary plant metabolites,
synthetic chemicals, inorganic ions and rancid fats taste bitter, bitterness
may be considered as a defense mechanism against the ingestion of potential
poisons (Meyerhof et al., 2005). The average human detection threshold for
quinine in water is 0.0008 mM.
Umami is the taste of certain amino acids, like glutamate, aspartate and
related compounds. It was first defined by Ikeda at the Imperial Univer-
sity of Tokyo in 1909. Monosodium glutamate, added to many foods to
enhance their taste, may stimulate the umami receptors. The average hu-
man detection threshold for glutamate in water is 0.07 mM. Umami taste
has characteristic qualities that differentiate it from other tastes, including a
taste-enhancing synergism between L-glutamate and 5’-ribonucleotides and
a prolonged after taste (Teranishi et al., 1999; Ninomiya, 2002).
Taste and the content of taste compounds in a fruit can be influenced
by several factors. Genetics, maturity, pre- and postharvest handling cause
significant changes in taste. Genetics is the predominant factor influencing
taste. The chemical content of fruits from different cultivars can differ sub-
stantially. Large differences in the amount of acids and sugars and their
ratio influence taste significantly (Petro-Truza, 1987; Baldwin et al., 1991).
By genetically manipulating the sugar and acid content of a fruit, the taste
will change significantly (Jones and Scott, 1983). Cultivar plays only a small
role in the total mineral content of a fruit (Davies and Winsor, 1967). Large
differences are present between ripe and unripe fruit. The changes which
occur during ripening mainly involve an increase of the sugar content and
a change of the total sugar and acid composition of the fruit (Davies and
Kempton, 1975; Petro-Truza, 1987). The condition under which the fruits
ripen influence the final composition of the fruits (Auerswald et al., 1999).
Of the evironmental factors, light has the most pronounced positive effect
on the sugar content of a fruit. As a consequence, greenhouse fruit grown
during winter contains substantially less sugar. During processing of fruit,
the sugar content can decrease depending on the time and extent of heat
General introduction 5
treatment. The loss can be explained by caramelization, browning reactions
between sugars and amino acids, and by the formation of 5-hydroxymethyl
furfural in an acid medium upon the loss of H2O2. The acid content of
a fruit is highly dependent of the soil conditions. The potassium content
of the soil affects the total acid content of a fruit (Petersen et al., 1998).
Davies and Winsor (1967) found a positive logaritmic correlation between
the potassium level in the soil and the total acid content of a fruit. The
glutamate and aspartate content in fruit is highly dependent on the nitro-
gen and phosphate conentent in the soil. A high nitrogen content and a low
phosphate content substantially increase the concentration of these amino
acids (Davies, 1964). Fruit grown in a field contains significantly more glu-
tamate than fruits grown in a greenhouse (Yamanaka et al., 1971).
1.3 Measurement of taste
Many fruit and vegetable are determined by their typical sweet and sour
taste. Various techniques are used to determine the sugar and acid content
of plant material. Sugars and acids can be determined by techniques such
as high performance liquid chromatography (HPLC) or, after derivatiza-
tion, gas chromatography(-mass spectrometry) (GC-MS). Both techniques
require expensive apparatus and demand a considerable amount of time per
measurement (Molnar-Perl, 1999). Another method of determining specific
sugars and acids is by enzymatic analysis. Assays are available for vari-
ous sugars, citric acid, malic acid and glutamic acid. For these enzymatic
assays, sample preparation is simple and the only apparatus required is a
spectrophotometer (Vermeir et al., 2007).
Next to these more traditional techniques to determine individual chem-
ical compounds, some recent developments are available which do not de-
termine individual compounds in a direct way. In the last decade, arrays
of sensors that analyze liquids, which are referred to as electronic tongues
(ET’s), were developed. Although the development of ET’s is still in its
early stages, several applications have already been described (Vlasov et al.,
2002; Toko, 1996; Winquist et al., 1997). ET’s have proved to be successful
in discrimination and classification of food products, food quality evalua-
6 1.4 Objectives and outline of the thesis
tion, and control and process monitoring. The main advantages of ET’s are
the low cost, easy-to-handle measurement set-up and speed of the measure-
ments (Vlasov et al., 2002; Deisingh et al., 2004). Despite the positive results
found for each ET in literature, no extensive study has been published on
the use of ET technology in high throughput experiments on horticultural
produce. Also, it is not known whether some types of ET’s perform better
than others.
Another alternative for traditional instrumental techniques is Fourier
transform infrared spectroscopy (FTIR), which is a well-established tech-
nique in chemical analysis. FTIR spectrometers using an interferometer
provide high energy to the sample, scan fast and have the capability to
co-add data so that within a short time spectra can be produced from
poorly transmitting samples with acceptable signal-to-noise ratios (Wilson
and Goodfellow, 1994). Mid-IR has significant advantages over NIR for
spectral assignment, resolution and ease of quantification. Another advan-
tage is that mid-IR spectra can provide information about the physical and
chemical states of individual compounds. If combined with attenuated to-
tal reflectance (ATR) and flow injection systems, this technique offers great
advantages for food analysis (Griffiths and de Haseth, 2007). As literature
shows, a lot of research has been performed to study the potential of ATR-
FTIR for the classification of samples and the determination of different
compounds. Until now, however, no report has been published regarding
the use of ATR-FTIR to measure taste of fruits. Also, there is scope for
improving sample presentation protocols.
1.4 Objectives and outline of the thesis
The main objective of this thesis is to study the potential of rapid and
objective measurement techniques for taste profiling of fruit and vegetable.
In order to achieve this goal, different subobjectives were defined.
� to evaluate the ET and (SIA-)ATR-FTIR as rapid instrumental tech-
niques for the classification of fruit samples based on their chemical
composition
General introduction 7
� to study the ability of the ET and (SIA-)ATR-FTIR to quantify in-
dividual taste compounds in fruit as measured using a reference tech-
nique
� to compare the potential of the ET and SIA-ATR-FTIR to determine
the taste of fruit as measured by a trained sensory panel
� to investigate the possibilities of the ET system and ATR-FTIR spec-
troscopy as tools for quality control of fruit juices.
The outline of this thesis is as follows. In Chapter 2, the state-of-the-art
of traditional and emerging instrumental techniques available for the analysis
of taste compounds and taste will be discussed. HPLC, enzymatic analysis,
atomic absorption spectroscopy (AAS), soluble solids content (SSC), titrat-
able acidity (TA), ET technology and (SIA-)ATR-FTIR will be introduced
briefly. Special attention will also go to taste analysis with trained panels,
as well as a selection of multivariate techniques to analyze the instrumental
and sensory datasets.
The potential of ET technology is discussed in Chapter 3. For the first
time, the ability of the ET developed at the University of Saint-Petersburg
(ETSPU) to classify and quantify taste compounds in apple and tomato
cultivars will be studied. The potential of this multisensor system to ana-
lyze tomato cultivars with very different taste profiles will be compared to
the commercially available ASTREE Liquid and Taste Analyzer from the
Alpha M.O.S. company. This will be the first comparison between ET’s
ever reported. Finally, the possibilities of the ETSPU as a tool for rapid
quality control will be studied. The results presented in this chapter are
published by Beullens et al. (2004, 2005a,b, 2006a, 2007a,b, 2008b), Legin
et al. (2005a) and Rudnitskaya et al. (2006).
Chapter 4 deals with the assessment of the potential of ATR-FTIR for
rapid analysis of taste compounds in apple and tomato fruit. Never before,
a structured study on the determination of taste compounds in fruit has
been reported. First, the ability of ATR-FTIR to measure taste compounds
will be studied using dilution series of three sugars and two acids. Second,
8 1.4 Objectives and outline of the thesis
the potential of ATR-FTIR to classify cultivars will be discussed. Third,
the possibility of ATR-FTIR to quantify individual chemical compounds
based on the absorbance spectrum of a sample will be studied. Finally, the
potential of ATR-FTIR as a tool for quality control of fruit juices will be
evaluated. The results of the experiments were published by Beullens et al.
(2004, 2005a,b, 2006a) and Rudnitskaya et al. (2006).
In Chapter 5 a novel sequential injection analysis (SIA) system will be
described to complement ATR-FTIR. The chapter will be divided into two
parts. The first part will deal with the development and optimization of
the SIA-ATR-FTIR technique using model solutions. The second part will
evaluate the system in an experiment using tomato samples with very dif-
ferent taste profiles. The results described in this chapter are described in
Beullens et al. (2006b, 2007c, 2008c).
The final challenge of this thesis will be the prediction of taste attributes
as measured by a trained panel using the ASTREE ET, ETSPU and SIA-
ATR-FTIR. The ability of all three techniques to predict sweetness, sour-
ness, saltiness and umami taste will be studied in Chapter 6. This will be
the first evaluation of SIA-ATR-FTIR as a technique to predict taste at-
tributes ever. The results of this work have been described in Beullens et al.
(2008b,c).
Finally, some general conclusions and suggestions for future work will be
formulated in Chapter 7.
Chapter 2
Literature review
2.1 Introduction
For the consumer, important quality attributes are flavor and food safety.
Flavor is the sensory impression of a food product which is determined
by both taste and aroma. The evaluation of flavor is generally very time
consuming, difficult and expensive. In this work, the focus will be on the
detection of taste in fruit.
Taste is a very subjective quality characteristic of food products. Five
gustatory perceptions, sweetness, sourness, saltiness, umami and bitterness,
determine the taste of a product (Meilgaard et al., 2007). The five taste
attributes are mainly caused by the presence of respectively sugars, organic
acids, salts, monosodium-glutamate and phenolics and alkaloids. Because of
some important synergistic effects, it is not possible to explain the sensation
of taste by the presence of individual chemical compounds (Stevens et al.,
1977; Salles et al., 2003).
Many fruit and vegetable are characterized by their typical sweet and
sour taste. Several techniques are used to determine the sugar and acid
content of plant material. Sugars and acids are often determined by high
pressure liquid chromatography (HPLC). This technique, however, requires
9
10 2.2 Traditional techniques for the measurement of taste
expensive apparatus and is very time consuming (Molnar-Perl, 1999). A
second method of determining specific sugars and acids is by enzymatic
analysis. For these enzymatic assays, sample preparation is easy and only a
spectrophotometer is required (Vermeir et al., 2007).
Next to the traditional techniques, some recent developments are avail-
able which do not determine individual compounds in a direct way. Sev-
eral applications of arrays of sensors, which are refered to as electronic
tongues (ET’s), in food technology have been published over the last two
decades (Vlasov et al., 2002; Toko, 1996; Winquist et al., 1997). Also Fourier
transform infrared spectroscopy (FTIR) combined with attenuated total re-
flectance (ATR) offers great advantages for the analysis of food products
(Griffiths and de Haseth, 2007). The objective of this chapter is to give an
overview of all instrumental, sensory and statistical techniques used in the
experimental part of this work and some available alternatives. In a first
part, traditional instrumental and sensory techniques available for the anal-
ysis of taste compounds and taste will be discussed (Section 2.2). Next, a
detailed study will be made on ET technology (Section 2.3) and ATR-FTIR
(Section 2.4). Finally, some techniques for multivariate statistical analysis
will be discussed (Section 2.5).
2.2 Traditional techniques for the measurement of
taste
2.2.1 High performance liquid chromatography
Since the early 1970s HPLC has evolved toward a high degree of technical
sophistication. Liquid chromatography (LC) has become a routine method
in almost all areas of instrumental analysis. LC has many applications
including separation, identification, purification and quantification of various
compounds. Chromatographic processes in general are defined as techniques
involving mass-transfer between stationary and mobile phases (Kazakevich
and Lobrutto, 2007). The LC instrument consists of a solvent reservoir,
Literature review 11
a pump, an injection device, a column, a detector and a data acquisition
system (Nollet, 1992).
The mobile phase in HPLC refers to the solvent which is continuously
pumped over the column or stationary phase. The mobile phase acts as
a carrier for the sample solution. The sample often undergoes a laborious
sample preparation like solvent extraction or purification before injection
into the loop via the injection valve (Salles et al., 2003; Kazakevich and
Lobrutto, 2007). As a sample solution flows through the column with the
mobile phase, the components bind to the column due to non-covalent inter-
actions with the column. The chemical interactions of the mobile phase and
sample with the column determine the degree of migration and separation
of components contained in the sample. Columns containing various types
of stationary phases are commercially available. After separation over the
column, the sample flows towards the detector. The detector is the compo-
nent that emits a response and signals a peak on the chromatogram. Some
of the more common detectors include the refractive index (RI) detector
for sugar analysis, ultraviolet (UV) and photodiode array (DA) detector for
acid analysis (Willard et al., 1988; Nollet, 1992).
2.2.2 Enzymatic analysis
Another method of determining specific compounds is by enzymatic analysis.
Assays are commercially available for various sugars and acids. For these en-
zymatic assays, sample preparation is simple and only a spectrophotometer
is required (Velterop and Vos, 2001). Enzymatic analysis works through the
use of specific enzymes which catalyze reactions with defined compounds.
This technique offers higher levels of sensitivity than chromatography. De-
spite all the positive aspects, commercial assays can be very expensive. The
cost per sample can be reduced by applying changes to the protocol to
enable determination of compounds in microtitre plates. Successful modi-
fication reduces the amount of sample, chemicals and enzymes needed and
increases the number of samples which can be analyzed per day (Campbell
et al., 1999; Vermeir et al., 2007).
12 2.2 Traditional techniques for the measurement of taste
2.2.3 Atomic absorption spectroscopy
Atomic absorption spectroscopy (AAS) is a technique for the determination
of the concentration of metal elements in a sample. AAS can be used to
analyze the concentration of over 62 different metals in a solution. The
technique typically uses a flame to atomize the sample. Three steps are
involved in turning a liquid sample into an atomic gas: desolvation, vapor-
ization and volatilization. A beam of light, which is produced by a hollow
cathode lamp, passes through the flame and hits a detector. A cylindrical
metal cathode containing the metal for excitation and an anode are present
in the lamp. The technique uses the wavelengths of light specifically ab-
sorbed by an element. They correspond to the energies needed to promote
electrons from one energy level to another, higher, energy level. It is possi-
ble to calculate how many energy transitions took place and, thus, find the
concentration of the element being measured (Haswell, 1991).
2.2.4 Soluble solids content and titratable acidity
The soluble solids content and titratable acidity of a sample are considered as
rapid and easily applicable techniques for the determination of respectively
the sugar and acid content. The amount of soluble solids is determined
by the refraction of visible light using a refractometer. The soluble solids
content of a fruit contains mainly sugars, however, also acids and other
compounds contribute to the refractive index. The amount of titratable
acidity is correlated with the total acidity of a sample. The method is
based on a titration with NaOH (0,1 ml/l). Despite the simplicity of these
techniques, they are limited by their non-specificity.
2.2.5 Sensory analysis
A traditional method for flavor analysis is the sensory evaluation. This
technique is used to measure those characteristics of foods and materials in
the way that they are perceived by the human senses. In the past decade,
Literature review 13
scientists have developed sensory testing as a formalized, structured and
codified methodology so that it serves economic interests. The principal
uses of sensory techniques are in quality control, product development and
research. Applications are not only found in characterization of foodstuff
and beverages, but also in other fields such as environmental odors, diag-
nosis of illnesses, testing of pure chemicals etc. The primary function of
sensory analysis is to conduct valid and reliable tests that provide data on
which decisions can be made (Meilgaard et al., 2007). Sensory methods can
be separated into two groups: discriminant methods and descriptive meth-
ods. The purpose of a discrimination test is simply to differentiate between
samples. Overall difference tests include triangle and duo-trio tests, which
are designed to show whether panelists can detect differences between all
samples (Piggott et al., 1998). Attribute difference tests deal with one or
a few attributes and how they differ between samples. All other attributes
are ignored. Examples of these tests are paired and multiple comparison
tests. Description tests, on the other hand, aim to identify and measure
the composition of products or to determine the presence or intensity of a
characteristic. The tests involve the detection and description of both qual-
itative and quantitative sensory aspects of a product by trained panelists.
Many descriptive analysis methods have been developed. The most popu-
lar are the flavor profile method, the texture profile method, the quantita-
tive descriptive analysis method, the spectrum descriptive analysis method,
time-intensity descriptive analysis and free-choice profiling (Hootman, 1992;
Meilgaard et al., 2007).
2.3 Electronic tongue technology
2.3.1 Introduction
Intensive research and development of sensors and sensor systems has been
carried out with respect to the analysis of food. In the 1980s, the first
multisensor system designed for aroma analysis, the ’electronic nose’, was
introduced (Gardner and Bartlett, 1994). The electronic nose design is based
14 2.3 Electronic tongue technology
on similarities with the biological olfactory system, comprising an array of
nonspecific receptors (sensors) and a neural network (natural or artificial)
for data processing. In analogy to the electronic nose technology, sensor
arrays for analysis in liquids were developed in the 1990s. These ’electronic
tongues’ (ET’s) consisted of ion-selective electrodes. This resulted in the
development of a ’taste sensor’, which was applied to the recognition of foods
and the establishment of a correlation between the sensors and the five basic
tastes (Toko, 1996). Different multisensor systems have been developed
over the last decade (Vlasov et al., 2002; Ciosek and Wroblewski, 2007).
The main advantages of ET technology are the low cost, easy-to-handle
measurement set-up and speed of the measurements (Lvova et al., 2003). In
the following part, the theoretical background of the different systems used
in the experimental part of this thesis will be discussed together with other
technologies and ET’s which have been proved to be successful.
2.3.2 Theory
There are two electrochemical measurement principles that are commonly
used in ET technology: potentiometry and voltammetry. Both require at
least two electrodes and an electrolyte solution. One electrode responds to
the target molecule and is called the working electrode, the second one is of
constant potential and is called the reference electrode (Pearce et al., 2003).
Potentiometry is a zero-current-based technique, in which a potential
across a membrane on the working electrode is measured. Different types
of membrane materials have been developed, having different recognition
properties. These types of devices are widely used for the measurement
of a large number of ionic species, the most important being the pH elec-
trode, other examples are electrodes for calcium, potassium, sodium, and
chloride. The equipment for potentiometric studies includes an ion-selective
electrode, a reference electrode and a potentiometer, as shown in Figure 2.1.
A commonly used reference electrode is the Ag/AgCl electrode based on the
half-cell reaction:
Literature review 15
V Reference electrode
Ion selective electrode
Ag/AgClAg/AgCl
Ion selective membrane
Voltage junction
Figure 2.1: Schematic set-up of potentiometric sensors (after Pearce et al. (2003)).
AgCl + e− −→ Ag + Cl− Eo = +220mV (2.1)
The electrode consists of an Ag wire coated with AgCl placed into a
solution of Cl− ions. A porous plug will serve as a voltage bridge to the
outer solution. The ion-selective electrode has a similar configuration, but
instead of a voltage bridge, an ion-selective membrane is applied. This
membrane should be non-porous, water insoluble and mechanically stable.
It should have an affinity for the selected ion that is high and selective.
Potentiometry generally assumes linear dependence between an electronic
reading and the logarithm of the activity of the primary ion in a solution.
The potential, E, follows the Nernst relation:
E = Eo − RTzF· ln ar
ao(2.2)
where Eo (V) is a constant for the system given by the standard electrode
potentials, R is the ideal gas constant (8.31 JK−1mol−1) , T is the tem-
perature (K), z is the the number of electrons transferred, F is the Faraday
16 2.3 Electronic tongue technology
constant (9.65 · 104Cmol−1) and ar and ao are the activities of the reduced
and oxidized species respectively (Deisingh et al., 2004). In case of a po-
tentiometric electronic tongue, the activity of the reduced species equals
one.
If there are interfering ions, the Nikolsky equation is used:
E = Eo + (RTziF
) · ln[ai + Kij(aij)zi/zj ] (2.3)
where Kij is the selective coefficient of the ion-selective membrane to the
primary ion i in the presence of an interfering ion j and Zi and Zj are the
charges of respectively the primary and interfering ions (Legin et al., 2002b;
Eggins, 2002; Pearce et al., 2003).
0
50
100
150
200
250
300
350
Pot
entia
l (m
V)
Time
Figure 2.2: Typical signal of potentiometric sensor.
A typical potentiometric signal is shown in Figure 2.2. The cycle followed
Literature review 17
in the ET measurement protocol is shown by subsequent high potentials
when the sensors are submerged in the sample and low potentials when they
are immersed in a cleaning solution.
Reference electrodevref
vDS
Ion selective membranes dg
Figure 2.3: Schematic diagram of ISFET (s: source, d: drain, g: gate) (after
Pearce et al. (2003)).
In the early 1970s, ion-selective field effect transistors (ISFET’s) were
developed, in which the ion-selective material is directly integrated with
solid-state electronics. A schematic diagram of an ISFET is shown in Figure
2.3. The current between the drain and source (IDS) depends on the charge
density at the semiconductor surface. This is controlled by the gate poten-
tial, which in turn is determined by ions interacting with the ion-selective
membrane. In the ISFET, the normal metal gate is replaced with the refer-
ence electrode and sample solution (Wang, 2006). An attractive feature of
ISFET’s is their small size and ability to be directly integrated with micro-
electronics. Furthermore, if mass fabricated, they can be made very cheaply.
These features make them especially valuable for use in ET’s. Potentiomet-
ric devices offer several advantages for use in ET’s or taste sensors. There
are a large number of different membranes available with different selectivity
18 2.3 Electronic tongue technology
properties, such as glass membranes and lipid layers. A disadvantage is that
the technique is limited to the measurement of charged species only (Pearce
et al., 2003).
In voltammetric techniques, the electrode potential is used to drive an
electron transfer reaction and the resulting current is measured. The size of
the electrode potential determines whether the target molecules will receive
or donate electrons. The reaction taking place at the electrode surface is:
O + ne− −→ R (2.4)
where O is the oxidized form and R is the reduced form of the ana-
lyte. At standard conditions, this redox reaction has the standard potential
Eo. The potential of the electrode, can be used to establish a correlation
between the concentration of the oxidized and the reduced form of the an-
alyte, according to the Nernst relation. Pulse voltammetry is of special
interest due to its great sensitivity. Two types of pulse voltammetry are
commonly used: large amplitude pulse voltammetry (LAPV) and small am-
plitude pulse voltammetry (SAPV) (Pearce et al., 2003). At the onset of
a voltage pulse, charged species and oriented dipoles will arrange next to
the surface of the working electrode, forming a Helmholz double layer. A
charging current will flow as the layer builds up. The redox current from
electroactive species is initially large when compounds close to the electrode
surface are oxidized or reduced, but decays with time when the diffusion
layer spreads out. Voltammetric methods can thus measure any chemical
species that is electroactive. Voltammetric methods provide high sensitiv-
ity, a wide linear range and simple instrumentation (Winquist et al., 1997;
Wang, 2006).
Most ET or taste sensors are based on potentiometry or voltammetry.
There are, however, also some other techniques that are interesting to use
and which have special features making them useful for ET’s, such as optical
techniques or techniques based on mass sensitive devices. These types of
devices are very general and have a wide potential for detecting a large
Literature review 19
number of different compounds (Eggins, 2002; Pearce et al., 2003).
2.3.3 Instrumentation
Three groups dominate the research on ET technology: Toko and co-workers
in Japan, Winquist and co-workers in Sweden and Legin and co-workers
in Russia. The most important characteristics of all three ET’s and the
commercially available ASTREE ET by Alpha M.O.S. (Toulouse, France)
are discussed below.
The first multisensor system for liquid analysis based on a non-specific
sensor approach is the taste sensor introduced in 1990 by Toko (Toko,
1998a,b, 2000a,b). The multichannel taste sensor is based on ion-sensitive
lipid membranes, immobilized on a PVC polymer. In the taste sensor, five
different lipids are used (n-decyl alcohol, oleic acid, dioctyl phosphate (bis-
2-ethylhexyl)hydrogen phosphate, trioctylmethyl ammonium chloride and
oleylamine) together with their mixtures (Pearce et al., 2003). In total eight
different membranes are used in the ET, where each electrode consists of a
Ag wire, with deposited AgCl, inside a 100 mM KCl solution (Toko, 1998a).
The voltage between a given electrode and a Ag/AgCl reference electrode is
measured. The taste sensor is used to study the five basic taste attributes:
sweetness (sucrose), sourness (HCl), saltiness (NaCl), bitterness (quinine)
and umami (monosodium glutamate). The largest responses were obtained
from the sour and bitter compounds, followed by umami and salty. For
sucrose almost no response was obtained (Toko, 2000a). The multichannel
system has been commercialized as the taste sensing system SA402 (An-
ritsu Corp., Japan) (Ivarsson et al., 2001). The detection element is an
eight-channel multisensor, placed on a robot arm and controlled by a com-
puter. The samples are placed in a sample holder together with a cleaning
solution and reference solutions. After cleaning the multisensor in a cleaning
solution, it is inserted in the sample solution. Every couple of hours, the
multisensor is placed in the reference solution for calibration purposes.
A second type of ET was developed at the Swedish Sensor Centre, S-
20 2.3 Electronic tongue technology
SENCE, at Linkoping University. The first voltammetric ET is based on
both LAPV and SAPV applied to a double working electrode, an auxiliary
and a reference electrode (Winquist et al., 1997). The double working elec-
trode consisted of one wire of Pt and Au. After further development in the
last decade, the most recent configuration consists of five working electrodes,
a reference electrode and an auxiliary electrode of stainless steel. Metal wires
of Au, Ir, Pd, Pt, and Rh used as working electrodes are embedded in epoxy
resin and placed around a reference electrode. The sensor is inserted in
a plastic tube ending with a stainless steel tube as an auxiliary electrode.
Different types of pulsed voltammetry can be applied, LAPV, SAPV and
staircase (Pearce et al., 2003). A hybrid ET has also been developed, based
on the combination of potentiometry, voltammetry and conductivity. The
hybrid ET consists of six working electrodes of different metals (Au, Ir,
Pd, Pt, Re and Rh), three ion-selective electrodes and a Ag/AgCl reference
electrode. The measurement principle is based on LAPV in which current
transients are measured (Winquist et al., 2000).
Figure 2.4: Electronic tongue developed at Saint-Petersburg University.
The third type of ET was developed at the University of Saint-Petersburg
Literature review 21
(ETSPU) in Russia (Figure 2.4). The main features of the sensors used in the
ET are their non-specificity and wide and reproducible cross-sensitivity to
different components in liquid media (Legin et al., 1997; Vlasov et al., 2000,
2002). The sensing materials include a wide range of chalcogenide glasses
doped with different metals, plasticized polyvinylchloride (PVC) polymers
containing various plasticizers and active substances such as ionophores,
neutral carriers, metalloporphyrins and crystalline compositions. The sensor
array typically comprise from 10 to 30 sensors, depending on the application.
Potentiometric measurements with the sensor array are commonly made
relative to the Ag/AgCl reference electrode, by use of a high input impedance
interface device (Vlasov et al., 2002; Legin et al., 2003). More information
on the chalcogenide ion selective electrodes is available in US patent 5464511
(Vlasov and Bychkov, 1995).
Figure 2.5: ASTREE electronic tongue developed by Alpha M.O.S.
During the nineties, Alpha M.O.S. (Toulouse, France) successfully devel-
oped electronic noses and tongues for the measurement of aroma and taste.
The ASTREE Liquid and Taste Analyzer (Figure 2.5) is made out of seven
sensors for liquid analysis, which are available in two different sets, with
22 2.3 Electronic tongue technology
a cross-selectivity to dissolved organic compounds in liquids (AlphaM.O.S.,
2006). The sensors are made from silicon transistors with an organic coating
that governs sensitivity and selectivity of each individual sensor. The differ-
ence between each sensor and a Ag/AgCl reference electrode is measured.
The system can be fully automated through a 16-position autosampler (Tan
et al., 2001). Since this ET is a commercial product, no details are available
on the sensor array (US patent 6290838) (Mifsud and Lucas, 2003).
2.3.4 Applications
The taste sensor developed by Toko has been applied for the quantification
of taste. The sensitivity of the taste sensor was studied in aqueous solutions
of the five basic tastes (Toko, 1998a). The researchers focused on tasting
umami and bitter substances. Suppression of bitter taste by the presence of
sweet substances, often used to mask bitter taste of drugs, has been studied
using the taste sensor (Takagi et al., 2001). An attempt was made to build
a taste map by expressing the tastes of food products combining the basic
tastes using the ET. Furthermore, the taste sensor was applied to tomato
juices (Kikkawa et al., 1993), commercial brands of sake (Iiyama et al., 2000),
brands of beer, coffee from different origins, commercial brands of mineral
water, milk with different kinds of heat treatment and wine from different
vineyards (Toko, 1998a,b, 2000b).
The voltammetric ET has been applied for the analysis of different food
products. Different beverages were studied using the first version of the ET:
nine brands of orange juice, two types of orange soft drinks, apple juice, tea,
drinking water and pasteurized milk (Winquist et al., 1997; Ivarsson et al.,
2001; Winquist et al., 2005). It was found that the ET could distinguish be-
tween different types of beverages. Ageing of beverages was evaluated using
the same sensor array. The voltammetric ET with five working electrodes
has been used to monitor milk souring and bacterial growth (Winquist et al.,
1998). The hybrid ET combining voltammetry with six working electrodes,
two ion-selective electrodes, a CO2 electrode and conductivity was applied
for the recognition of fermented milk (Winquist et al., 2000). This ex-
Literature review 23
periment proved that the combination of voltammetric, potentiometric and
conductometric signals improves the performance of the ET.
The ETSPU has shown its potential in both quantitative measurements
and classification. The sensitivity of more than 40 different sensing materials
to organic taste substances, present in many food products, has been eval-
uated (Vlasov et al., 2002). For each class of taste substance sensors with a
meaningful and reproducible response were identified. The ET was applied
to quantitative and qualitative analysis of numerous products. Recognition
of mineral waters has been described (Legin et al., 1999b, 2000). Some
mineral waters were deliberately polluted by organic matter and the con-
tamination was detected by means of the ET. The ET was applied to dis-
criminate between fruit juices and to monitor juice spoilage (Rudnitskaya
et al., 2001). Milk samples subjected to different heat treatments and from
different manufacturers were studied qualitatively and differences in sour-
ing between UHT and pasteurized milk were detected using the multisensor
system (Legin et al., 1997). The ability of the ET to distinguish between
regular and diet cokes and experimental coke mixtures has been evaluated
(Legin et al., 2002a). The results were correlated to sensory panel scores
in a preference mapping study. Analysis of Italian wine showed that the
ET could discriminate between wines from different origins. The ET could
determine the alcohol and organic acid content of wine, together with the
total acidity and pH (Legin et al., 1999b).
The ASTREE, in combination with the electronic nose developed by
Alpha M.O.S., was used to classify food and beverages, determine bitterness
in coffee (AlphaM.O.S., 2006) and predict 34 sensory characteristics of apple
juice, like aroma, taste, color, flavor, mouthfeel, aftertaste, etc. (Bleibaum
et al., 2002).
24 2.4 Fourier transform infrared spectroscopy
2.4 Fourier transform infrared spectroscopy
2.4.1 Introduction
In 1800 Sir William Herschel, an astronomer, made the important discovery
of infrared (IR) light in the first experiment which showed there were forms of
light invisible to the human eye. The development of FTIR would have been
impossible without the discovery of this IR light and the development of the
Michelson interferometer. This optical device was invented in 1880 by Albert
Abraham Michelson, who was awarded the Noble Prize in Physics in 1907 for
accurately measuring wavelengths of light using his interferometer. At first
interferograms were measured manually, but the invention of the computer
and the fast Fourier transform (FFT) by J.W. Cooley and J.W. Tukey were
the breakthrough that made FTIR possible. The first commercially available
FTIR spectrophotometers were manufactured at the Digilab of Cambridge
University (Massachusetts, USA) due to the efforts made by P. Griffiths,
R. Curbelo, L. Mertz and many others. The first instruments made the
acquisition of qualitative high resolution data in a short period of time and
established the advantages of FTIR over previous means of obtaining IR
spectra (Smith, 1996).
In the past, the mid-IR region (4000 cm−1-400 cm−1) has been of min-
imal interest to the food analysist. Most foods contain large amounts of
water that strongly absorbs mid-IR radiation. The poor transmission and
often high scattering of many samples means that conventionally very little
light can be detected. The development of FTIR spectroscopy has renewed
interest in the potential of mid-IR for food analysis. FTIR spectrometers
using an interferometer provide more energy to the sample, scan a lot faster
and have the capability to co-add data so that within a short time spectra
can be produced from poorly transmitting samples with acceptable signal-
to-noise ratios. Mid-IR has significant advantages over near-IR for spectral
assignment, resolution and ease of quantification. Another advantage is
that mid-IR spectra can provide information about the physical and chemi-
cal states of individual compounds (Wilson and Goodfellow, 1994; Chalmers
Literature review 25
and Griffiths, 2002; Griffiths and de Haseth, 2007). The theoretical back-
ground of FTIR will be discussed in the next part.
2.4.2 Theory
Mid-IR spectroscopy involves the molecular absorption of radiation between
4000 cm−1-400 cm−1. Figure 2.6 shows the different parts of the spectral
region, indicating the position of the IR region. IR spectra result from tran-
sitions between quantized vibrational energy states. Molecular vibrations
can range from the simple coupled motion of the two atoms of a diatomic
molecule to the much more complex motion of each atom in a large poly-
functional molecule. Molecules with N atoms have 3N degrees of freedom,
of which three represent translational motion in mutually perpendicular di-
rections and three represent rotational motion around the axes of inertia.
The remaining 3N -6 degrees of freedom give the number of ways that the
atoms can vibrate in a nonlinear molecule. The energy difference for transi-
tions between the ground state (ν = 0) and the first excited state (ν = 1) of
most vibrational modes corresponds to the energy of radiation in the mid-
IR spectrum. Figure 2.7 shows the potential energy of a diatomic molecule
as a function of the distance between the atoms. Simple harmonic motions
obey Hooke’s law, while anharmonic motions in practice show a Morse type
potential function (Griffiths and de Haseth, 2007).
The absorption arises through transitions between vibrational and rota-
tional energy states of the molecule. Different types of molecular vibrations
are shown in Figure 2.8. Since the absorption bands depend on the masses
of the atoms and the force constant of the vibrating bonds, it is possible
to assign absorptions to specific chemical entities. Furthermore, the inten-
sity of the absorption is proportional to the concentration of the absorbing
species. This makes IR spectroscopy a very useful technique for qualitative
and quantitative analysis.
26 2.4 Fourier transform infrared spectroscopy
Fig
ure
2.6
:S
pec
tralre
gio
ns
ran
gin
gfr
om
NM
Rtoγ
-ray
(Gri
ffith
san
dd
eH
ase
th,
2007
).
Literature review 27
r
V(r
)
0
Harmonic potential (Hooke’s law)
Anharmonic potential (Morse type)
ν = 0ν = 1ν = 2
Figure 2.7: Potential energy of a diatomic molecule during vibration for a har-
monic oscilator (dashed line) and an anharmonic oscilator (solid line) (after Griffiths
and de Haseth (2007)).
The basis of most quantitative analysis in optical techniques is the Beer-
Lambert relationship:
I = I0 · 10−εcl (2.5)
where I is the light intensity measured by the detector after passing
through the sample, I0 is the incident light intensity, ε is the molar extinc-
tion coefficient, c is the chromophore concentration and l is the light path
length through the sample. For quantitative analysis, often, IR spectra are
presented in absorbance units (A) defined as
A = − log10
II0
= εcl (2.6)
In the mid-IR well-resolved bands can often be identified as originating
28 2.4 Fourier transform infrared spectroscopy
+ + + -
(a) Stretching vibrations
(b) Bending vibrations
Symmetric Asymmetric
In-plane rocking
Out-of-plane wagging
In-plane scissoring
Out-of-plane twisting
Figure 2.8: Stretching and bending vibrations (after Griffiths and de Haseth
(2007)).
Literature review 29
from specific compounds, so that the Beer-Lambert relationship sometimes
can be used directly in FTIR. In complex mixtures, however, simple Beer-
Lambert relationships cannot be used. In such cases highly overlapping
bands may be present so that the absorbance at a given wavelength no
longer arises from a single component. Multivariate analysis can offer a
solution (Griffiths and de Haseth, 2007).
2.4.3 Instrumentation
2.4.3.1 Michelson interferometer
The main part of a FTIR spectrometer is a Michelson interferometer (Figure
2.9). An interferometer consists of a fixed mirror, a movable mirror and a
beam splitter. An IR beam radiated by a source is divided at the beam
splitter. Half of the light is send to a fixed mirror and half is reflected onto
a moving mirror. The two light beams are returned to the beam splitter
where they are combined and directed to the sample and the detector. If
the two mirrors are at equal distance from the beam splitter, the light is in
phase, resulting in constructive interference (Gunasekaran, 2001). Maximum
energy is then redirected by the beam splitter. If the movable mirror is
moved a distance l (mm), a path length difference δ (mm) is introduced in
one part of the interferometer. For a monochromatic light source destructive
interference occurs when δ = (n + 1/2)λ, where λ is the wavelength of the
light (µm). Constructive interference is found when δ = λ, 2λ, 3λ, ...,nλ. As
the mirror is moved, a fluctuating cosine wave, called an interferogram, is
seen as detector output. P(δ), the energy detected at the detector is given
by
P(δ) = B(σ) cos 2πδσ (2.7)
where B(σ) takes imperfections in the optical design into account, δ is
the path length difference and σ is the wavenumber of the light (cm−1), with
σ = 1/λ.
30 2.4 Fourier transform infrared spectroscopy
Direction of travel
Movable mirror
Fixed mirror
Detector
Source
Beamsplitter
Figure 2.9: Schematic view of Michelson interferometer (after Griffiths and
de Haseth (2007)).
In a true IR source many frequencies are present and the interferogram
is the sum of an infinite number of cosine waves, so that
P(δ) =∫ +∞
0B(σ) cos 2πδσ.dσ (2.8)
An interferogram contains all information of a spectrum but it is not
easy to interpret. A Fourier transform of the data provides a spectrum,
which shows the variation of the intensity as a function of the wavenumber
(Equation 2.9). In FTIR an interferogram is thus collected and transformed
into a spectrum.
P(σ) =∫ +∞
−∞B(δ) cos 2πδσ.dδ (2.9)
Literature review 31
where P(σ) is the energy incident at the interferometer (Wilson and
Goodfellow, 1994).
2.4.3.2 Attenuated total reflectance
The development of FTIR has led to an increased interest in sample pre-
sentation techniques. Today there is a wide choice of sample accessories
available with different designs and approaches. The main categories are
transmission methods, internal reflectance, diffuse reflectance, photoacous-
tic detection, Raman spectroscopy and GC/IR (Gunasekaran, 2001; Griffiths
and de Haseth, 2007). Since internal reflection is used in the experimental
part of this thesis, an overview of the theoretical background is given next.
Internal reflection, also known as attenuated total reflectance (ATR), is
one of the most powerful FTIR methods because of its flexible sample pre-
sentation. ATR accessories are available in many configurations for specific
applications. The internal reflectance element is the main component of
an ATR cell. In most cases it is a prism of IR transmitting material with
a high refractive index. An overview of commonly used ATR materials is
given in Table 2.1. Factors influencing the choice of ATR crystal include
the spectral range, refractive index of the crystal material and the sample,
pH range, angle of incidence and efficiency of sample contact (Griffiths and
de Haseth, 2007).
Horizontal ATR (HATR) accessories comprise a parallelogram prism
with mirrored ends. A spectrum can be acquired when a sample is spread
over the crystal (Figure 2.10). When light passes from one medium to an-
other the angle at which the radiation is refracted is described by Snellius’
law. The prism face is cut at an angle such that the light passes into the
prism at a predetermined angle. This angle θi is larger than the critical
angle θc given by
θc = sin−1 n2
n1(n1 > n2) (2.10)
32 2.4 Fourier transform infrared spectroscopy
Table
2.1
:O
pti
cal,
mec
han
ical
an
dch
emic
al
pro
per
ties
of
com
monly
use
dA
TR
cryst
alm
ater
ials
.
Mat
eria
lR
efra
ctiv
eSp
ectr
alH
ardn
ess
pHC
omm
ents
Typ
ical
sam
ples
inde
xra
nge
(kg.
mm−
2)
rang
e
(cm−
1)
ZnS
e2.
4020
000-
650
120
5-9
Mos
tpo
pula
rA
TR
mat
eria
l.O
rgan
icso
lven
ts,
past
es,
Wit
hsta
nds
limit
edm
echa
nica
lge
ls,
oils
,so
ftpo
lym
ers
and
ther
mal
shoc
k.an
dpo
wde
rs,
Can
besc
ratc
hed.
sam
ples
cont
aini
ngw
ater
.
Ge
4.00
5500
-830
780
1-14
Har
dan
dbr
ittl
e,te
mpe
ratu
reR
ubbe
ran
dot
her
carb
on
sens
itiv
e,su
bje
ctto
ther
mal
fille
dpo
lym
eric
mat
eria
ls,
shoc
k.E
xcel
lent
for
high
lyhi
ghab
sorb
ers,
solid
mat
eria
ls
abso
rbin
gsa
mpl
es.
and
othe
rty
pica
lsa
mpl
es.
Dia
mon
d2.
4130
000-
525
5700
1-14
Har
d,sc
ratc
h-re
sist
ant
mat
eria
l,U
sed
toan
alyz
eha
rdpo
wde
rs
/ZnS
esu
itab
lefo
rap
plic
atio
nsin
volv
ing
and
othe
rdi
fficu
ltto
aw
ide
rang
eof
chem
ical
s.an
alyz
eso
lidsa
mpl
es.
The
mos
tex
pens
ive
AT
Rm
ater
ial.
AM
TIR
2.50
1100
0-72
517
01-
9Se
leni
um,
arse
nic
&ge
rman
ium
glas
s.O
rgan
icso
lven
ts,
past
es,
gels
,
Rel
ativ
ely
hard
,br
ittl
e,so
ftpo
lym
ers
and
pow
ders
,
may
beda
mag
edun
der
sam
ples
cont
aini
ngw
ater
.
unev
enpr
essu
re.
Goo
dfo
rw
orki
ngw
ith
acid
icsa
mpl
es.
Literature review 33
where n2 is the refractive index of the surrounding medium and n1 is the
refractive index of the prism material (Chalmers and Griffiths, 2002).
MirrorInput beam Output beam
Trough
Internal reflection element
Figure 2.10: Schematic view of horizontal ATR accessory (after Griffiths and
de Haseth (2007)).
When θi > θc, total internal reflectance occurs at the interface. A num-
ber of reflections occur in the crystal before the light leaves the crystal. At
each point of internal reflection an evanescent wave is produced and some
radiation penetrates into the surrounding medium and can interact with
this medium. Attenuation of the reflected light results when the medium
absorbs IR light. The number of reflections and the depth of penetration
at each reflection (dp) determines the effective path length of the ATR cell.
The number of reflections, typically between 1 and 10, is determined by θi
and the dimensions of the prism. The penetration depth is given by
34 2.4 Fourier transform infrared spectroscopy
dp =λ
2πn1[sin2 θi − (n2/n1)2]12
(2.11)
where λ is the wavelenght of the light (Wilson and Goodfellow, 1994).
Figure 2.11: Tensor 27 FTIR and AMTIR ATR cell.
2.4.3.3 Flow injection analysis
Flow injection analysis (FIA) is a well established flow based technology
that brought speed, automation of solution handling, miniaturization and
low cost to the analytical laboratory during the last 30 years. The technique
was first applied by Ruzicka en Hansen in Denmark in 1974 (Ruzicka and
Hansen, 1975). Since then FIA has undergone some changes and modifica-
tions which can be classified as three generations: (i) FIA, (ii) sequential
injection analysis (SIA) and (iii) bead injection (BI) - lab-on-valve (LOV)
(Hansen and Wang, 2004).
The first generation of FIA was defined as ’the sequential insertion of
discrete sample solution into an unsegmented continuous flowing stream with
Literature review 35
subsequent detection of the analyte’ by Ruzicka and Hansen (1975). In the
simplest form of FIA the sample is injected into a continuous flow of a
reagent solution, dispersed and transported to a detector, a set-up which
shows some similarity with chromatography. The advantage of FIA is that
any number of additional lines with reagents can be added and any type of
detector can be used (Hansen and Wang, 2004).
In the second generation flow injection analysis has evolved into SIA.
This technique allows miniaturization of the set-up with as a consequence
a reduction of the consumption of sample and reagent solutions and hence
leads to generation of minute amounts of waste (Gallignani and Brunetto,
2004). While most FIA procedures use continuous and uni-directional pump-
ing of carrier and reagent streams, SIA is based on using programmable
bi-directional discontinuous flow, precisely controlled by a computer. Flow
programming is a unique tool that makes solution handling in the micro
scale possible (Ruzicka and Marshall, 1990). SIA has been applied to the
analysis of a wide variety of analytes in matrices as diverse as food, bever-
ages, bioprocesses, environmental, pharmaceutical and industrial processes
(Hansen and Wang, 2004). A general schematic flow diagram of a sequen-
tial injection analyzer is shown in Figure 2.12. The basic components of
the system, a pump with only one carrier stream, a single selection valve, a
single channel and a detector, are indicated (van Staden and Stefan, 2004).
In this work, an flow injection system based on the principles of SIA will be
developed.
The third generation of FIA, LOV, has many of the features of SIA. Here,
an integrated microconduit is placed on top of the selection valve. This
microconduit is designed to handle all the necessary operations required for
a given assay and thus acts as a small laboratory. LOV with integrated
BI has proven to be an attractive methodology in many contexts. BI uses
programmable flow to handle precise volumes of suspended microbeads that
serve as a carrier for reagents or analytes. It allows automated renewal of
the solid state phase, which is a critical feature when assay surfaces become
contaminated or dysfunctional, such as immunoassays (Ruzicka, 2000; Wang
and Hansen, 2003).
36 2.4 Fourier transform infrared spectroscopy
Figure 2.12: Schematic flow diagram of a basic sequential injection analyser. S
= sample; R = reagent; SV = selection valve; HC = holding coil; RC = reaction
coil; D = detector.
2.4.4 Applications
ATR-FTIR has proved to be a very attractive sampling tool, since many
applications are reported in literature. Applications are not only found in
the characterization of food products and beverages, but also in other fields
such as determination of products used for art purposes (Peris-Vicente et al.,
2007), medical research (Liu and Webster, 2007; Xu et al., 2007), fermenta-
tion monitoring (Roychoudhury et al., 2006), quality control and determina-
tion of pesticides (Armenta et al., 2005b,c; Khanmohammadi et al., 2007),
determination of surfactants in shampoo and soap (Carolei and Gutz, 2005),
quality control in PVC manufacturing (Bodecchi et al., 2005) and design of
food packaging (Irudayaraj and Yang, 2002; Lagaron et al., 2007). Both
qualitative and quantitative studies involving food products have been per-
Literature review 37
formed using ATR-FTIR. Many examples have been reported using various
food products ranging from fruit and vegetable (Garrigues et al., 2000; Ar-
menta et al., 2005a; Dogan et al., 2007) to their manufactured products
like fruit juices and oils (Inon et al., 2003; He et al., 2007). ATR-FTIR
has also widely been used for the analysis of beverages like beer and wine
samples (Edelmann et al., 2003; Moreira and Santos, 2004) and other drinks
(Paradkar and Irudayaraj, 2002; Irudayaraj and Tewari, 2003; Moros et al.,
2005).
FIA combined with ATR-FTIR has been applied successfully for the
determination of diverse analytes in different liquid matrices (Lendl and
Kellner, 1995; Daghbouche et al., 1997; Ayora-Canada and Lendl, 2000).
The use of enzyme reactors has been introduced in FIA in combination with
ATR-FTIR and is applied in the same fields.
2.5 Multivariate analysis techniques
Multivariate data analysis refers to any statistical technique used to analyze
multiple responses. Multivariate techniques are used in analysis to perform
studies across multiple dimensions while taking into account the effects of
all variables on the responses of interest (Sharma, 1996). Two types of
data should be analyzed using multivariate statistics: high dimensional data
(e.g. spectral data) with strong correlations between variables and data of
which different variables are measured individually (e.g. HPLC and sensory
analysis). In this part, first, different data pretreatment techniques which
are applied in IR spectroscopy are discussed (Martens and Naes, 1998; Naes
et al., 2004). Next, multivariate techniques dealing with both classification
and quantification are briefly discussed.
38 2.5 Multivariate analysis techniques
2.5.1 Data pretreatment
2.5.1.1 Averaging and centering
By averaging over samples, in case of replicates, or over variables, of the
same sample, the data are shown as their best estimation. When data are
mean centered, the average is subtracted from each variable. All results are
then interpretable in terms of variation around the mean (Naes et al., 2004).
2.5.1.2 Smoothing
Smoothing techniques include moving average filters and the Savitzky-Golay
algorithm to remove noise from IR spectra. Both techniques improve the
visual aspects of the spectra, but can remove information while it is not
clear yet whether this information is useful (Naes et al., 2004).
2.5.1.3 Standardization
Standardization is performed by dividing the spectrum at every wavelength
by the standard deviation of the spectrum at this wavelength. Typically,
variances of all wavelengths are standardized to 1, which results in an equal
influence of the variables in the model (Naes et al., 2004).
2.5.1.4 Normalization
Multiple scatter correction (MSC) is one of the most popular normaliza-
tion techniques. It is used to compensate for additive and multiplicative
effects in the spectral data, which are induced by physical effects such as
the non-uniform scattering throughout the spectrum. The method attempts
to remove the effects of scattering by linearizing each spectrum to some ideal
spectrum of the sample, which usually corresponds to the average spectrum.
Extended multiple signal correction (EMSC) is an extension of MSC and al-
lows to compensate also for chemical interference effects by incorporating
Literature review 39
known spectra of the interferents and analytes (Martens and Stark, 1991).
In standard normal variate correction (SNV), each individual spectrum is
normalized to zero mean and unit variance (Martens and Naes, 1998).
2.5.1.5 Transformations
Taking a first or second derivative can solve problems of baseline shifts and
superposed peaks. Derivative spectra of first order correct for additive ef-
fects. Derivative spectra of second order are very popular as they correct for
both additive and multiplicative effects. Both derivatives are usually calcu-
lated according to the Savitzky-Golay or Norris algorithm. The parameters
of the algorithm should be selected carefully in order to avoid amplification
of spectral noise (Martens and Naes, 1998; Naes et al., 2004).
2.5.2 Principal component analysis
Principal component analysis (PCA), an unsupervised technique, is one
of the most often used chemometric methods for data reduction and ex-
ploratory analysis on high dimensional data sets. The main goal of PCA is
to obtain a small set of principal components (PC) that are linear combina-
tions of the original variables and contain most of the variability on these
data sets. The first PC accounts for the maximal variance in the data, the
second PC accounts for the maximal variance that has not been accounted
for by the first PC and so on. The new subspace defined by these PC’s
leads to a model that is easier to interpret than the original data set, since
only a few PC’s need to be used to represent the multivariate data structure
rather than all variables. This technique is highly suitable for data explo-
ration (reduction of variables) and quality control (process control) since
data visualization is made easy using score plots and correlation loadings
plots (Sharma, 1996).
40 2.5 Multivariate analysis techniques
2.5.3 Principal component regression
Principal component regression (PCR) is a two-step procedure which first
decomposes the X-matrix using a PCA and then fits a multiple linear re-
gression (MLR) model, using a small number of PC’s instead of the original
variables as predictors. Usually a small number of uncorrelated PC’s is suf-
ficient. A drawback is that the PC’s are ordered according to decreasing
explained variance of the spectral matrix and that the first PC’s which are
used for the regression model are not necessarily the most informative with
respect to the Y-variable.
2.5.4 Partial least squares
Partial least squares regression (PLS) is a procedure that generalizes and
combines features from principal component analysis and multiple regression
and is used to model the relationship between a set of predictor variables
(X) and a set of response variables (Y) with explanatory or predictive pur-
pose. The Y-variables are actively used to estimate the partial least squares
components (PC’s) to ensure that the first components are those that are
most relevant for predicting the Y-variables. The interpretation of the re-
lationship between the X-data and Y-data is simplified as this relationship
is concentrated on the smallest possible number of PC’s. A full description
of the NIPALS algorithm used for PLS analysis is given by Trygg and Wold
(2002). The method performs particularly well when the various X-variables
express common information, i.e. when there is a large amount of correla-
tion, or even co-linearity, which is the case for spectral data (Geladi and
Dabakk, 1995).
2.5.4.1 PLS1 and PLS2
There are two versions of the PLS algorithm: PLS1 and PLS2. The differ-
ences between these methods are subtle but have very important effects on
the results. In PLS1, a separate model is calculated for each constituent of
Literature review 41
interest. In this case, the separate sets of eigenvectors and scores are specif-
ically tuned for each constituent. PLS regression can be easily extended to
the simultaneous prediction of several quality attributes. The algorithm is
then called PLS2. In PLS2, the calculated vectors are not optimized for
each individual constituent, but for all constituents simultaneously. This
may sacrifice some accuracy in the predictions of the constituent concen-
trations, especially for complex sample mixtures. The speed of calculation,
however, is an advantage of PLS2. Since no separate set of eigenvectors and
scores must be generated for every constituent of interest, the calculations
will be less time consuming than in PLS1 (Martens and Naes, 1998; Naes
et al., 2004).
2.5.4.2 Partial least squares-discriminant analysis
Partial least squares discriminant analysis (PLS-DA), a supervised tech-
nique, is used to classify samples. PLS-DA is a regression extension of PCA
that takes advantage of class information to maximize the separation be-
tween groups of observations. The technique consists in a classical PLS
regression where the response variable is a categorical variable, which is re-
placed by the set of dummy variables describing the categories, expressing
the class membership of the sample. PLS-DA does not allow other response
variables than the ones that define the groups of samples. As a consequence,
all measured variables play the same role with respect to the class assign-
ment. PC’s are built to find a compromise between two purposes: describing
the set of explanatory variables and predicting the response variables (Naes
et al., 2004).
2.5.4.3 Model validation and accuracy
In order to assess the accuracy of the calibration model and to avoid over-
fitting, validation procedures have to be applied. Leverage correction is an
equation based procedure to estimate the prediction accuracy without per-
forming any prediction and is to be avoided at all times because it always
42 2.5 Multivariate analysis techniques
leads to overoptimistic estimates. In leave-one-out cross validation, one sam-
ple is removed from the dataset and a calibration model is constructed for
the remaining subset. The removed samples are then used to calculate the
prediction residual. The process is repeated with other subsets until every
sample has been left out once and in the end the variance of all prediction
residuals is estimated. In multifold cross validation, a well-defined number
of samples are left out of the calibration set instead of one. The model is
validated using the left-out samples (Martens and Naes, 1998).
In internal validation, the dataset is split into a calibration and a vali-
dation set. The calibration model is constructed using the calibration set,
and the prediction residuals are then calculated by applying the calibration
model to the validation set. In external validation, the validation dataset
is independent from the calibration dataset (Martens and Naes, 1998; Naes
et al., 2004).
Prediction models having a correlation between the predicted and mea-
sured value of a variable above 0.90 are considered to be excellent, if the
slope and offset are close to 1 and 0 respectively. The correlation, slope and
offset give information about the quality of the model, but give no direct
information about the prediction accuracy. The root mean square error of
cross validation (RMSECV) (Equation 2.12) and ratio of prediction to devi-
ation (RPD) (Equation 2.13) values are often used to assess the performance
of a model to predict a variable (Geladi and Dabakk, 1995).
RMSECV =
√√√√ 1n
n∑i=1
(ci − ci)2 (2.12)
where ci is the known concentration, ci is the predicted concentration
and n is the total number of samples for the cross validation dataset.
RPD =St.Dev.
RMSECV(2.13)
where St.Dev. is the standard deviation of all samples for the compound
studied. An RPD value below 1.5 indicates that the model is not usable.
Literature review 43
An RPD value between 1.5 and 2 reveals a possibility to distinguish between
high and low values, while a value between 2 and 2.5 makes approximate
quantitative predictions possible. For values between 2.5 and 3, and above
3, the prediction is classified as good and excellent, respectively (Saeys et al.,
2005).
44 2.5 Multivariate analysis techniques
Chapter 3
Electronic tongue technology
3.1 Introduction
Since flavor is an important quality attribute, there is a need for rapid, low
cost and simple methods of flavor analysis and consumer quality assessment.
After intensive research a first rapid multisensor system for aroma analysis
was developed in the beginning of the 1980s (Gardner and Bartlett, 1994).
Similar to these arrays of sensors for the analysis of gases, called electronic
noses (EN), arrays of sensors for liquid analysis were developed in the last
decade. Electronic tongues (ET’s) are defined as ’multisensor systems for
liquid analysis based on chemical sensor arrays’ by Legin et al. (2002b).
Although the development of ET’s is still in its early stages, they have
proved to be successful in discrimination and classification of food prod-
ucts, food quality evaluation, control monitoring. The main advantages of
ET’s are the low cost, easy-to-handle measurement set-up and speed of the
measurements (Vlasov et al., 2002; Deisingh et al., 2004). In fruit quality
research, the ET has been used successfully for the classification and deter-
mination of fruit juices (Legin et al., 1997; Bleibaum et al., 2002; Gallardo
et al., 2005). Legin et al. (1997) used their system for the discrimination
between various sorts of the same type of beverages and the monitoring of
the aging process of juices. Orange based drinks were classified using a po-
45
46 3.1 Introduction
tentiometric ET, showing possibility to determine the natural juice content
of the samples (Gallardo et al., 2005). Research was conducted to compare
apple juice quality evaluated by consumers, a trained sensory panel and in-
strument analysis using the ASTREE ET (Alpha M.O.S., Toulouse, France)
and the Prometheus EN (Alpha M.O.S., Toulouse, France) (Bleibaum et al.,
2002).
Until now no extensive study has been published on the use of ET tech-
nology in high throughput experiments on horticultural produce. The ET
could be a promising technique in large scale experiments. In breeding or
cultivar selection programs, large amounts of fruit need to be analyzed in a
very short time period. Due to its measurement speed, ET technology could
be applicable in online analysis during these experiments. Also, there are no
published data about the performance of different types of ET’s. Neverthe-
less, such information is crucial for the applicability of ET’s to the analysis
of fruit.
The objective of this chapter is to study the potential of ET’s as rapid
techniques for the high throughput analysis of taste compounds in fruit and
vegetable. Hereto three experiments will be carried out.
� In a first experiment, the potential of the ET developed at Saint-
Petersburg University (ETSPU) to classify apple and tomato cultivars
according to their sugar and acid profile and to quantify their chemical
content will be studied. Hereto, first, the information content of the
ETSPU is compared to that of the HPLC reference technique. Next,
the ability of the ETSPU to predict the chemical composition of the
apples and tomatoes is investigated.
� In a second experiment, the potential ETSPU will be compared to the
commercially available ASTREE ET of Alpha M.O.S. for rapid qual-
itative and quantitative determination of taste compounds in Belgian
tomato cultivars. The samples were, next to the ET, also analyzed us-
ing sensory panels. The results of these panel measurements in relation
to the ET results will be discussed in Chapter 6.
Electronic tongue technology 47
� In a final experiment, the ability of the multisensor system as a tool
for quality control in food industry will be evaluated, since errors in
the production process of all food products should be detected as soon
as possible. Different multifruit juices and the individual syrups they
are composed of are analyzed using the ETSPU. The ability of the ET
to detect differences in the fruit composition of the multifruit juices
will be studied.
This chapter is divided in four main sections. In Section 3.2 the materials
and methods are described. The results of the experiments with tomatoes,
apples and fruit juices are given in Section 3.3. First, the results of the
classification and quantification experiment using apples and tomatoes are
shown. Second, the results of the experiment in which the comparison of
two types of ET’s is made are presented. Finally, the results of the quality
control experiment of fruit juices are shown. In Section 3.4 the results
are discussed and compared to literature findings. Concluding remarks are
formulated in Section 3.5. The results presented in this chapter have been
published by Beullens et al. (2004, 2005a,b, 2006a, 2007a,b, 2008b,a), Legin
et al. (2005a) and Rudnitskaya et al. (2006).
3.2 Materials and methods
3.2.1 Samples
3.2.1.1 Classification of apple and tomato cultivars and quantifi-
cation of taste compounds
An artificial fruit juice was made using pure compounds which determine the
taste of fruit. The basic composition of the artificial juice is shown in Table
3.1. The chemical compounds were purchased at Sigma Aldrich (Steinheim,
Germany). Different concentrations of citric acid, malic acid and glucose
were added to the basic artificial juice for calibration purposes. These three
compounds were chosen based on their importance in tomato (Petro-Truza,
48 3.2 Materials and methods
Table 3.1: Composition of the artificial juice made to calibrate the ETSPU.
Compound Artificial juice
Glucose 11 g/L
Fructose 7 g/L
Sucrose 0.9 g/L
Citric acid 2 g/L
Malic acid 0.8 g/L
Oxalic acid 0.2 g/L
Succinic acid 1 g/L
Tartaric acid 1 g/L
K2HPO4 1900 mg/L
Na2HPO4.12H2O 60 mg/L
CaCl2.2H2O 30 mg/L
MgCl2.6H2O 800 mg/L
KOH 9 mg/L
1987). After preparation of the mixtures, they were frozen in falcon tubes
using liquid nitrogen and frozen at −80 ◦C until analysis.
Apples (Malus× domestica Borkh.) of five cultivars were used in the
first experiment: Cox, Elstar, Golden Delicious, Jonagold and Pinova. The
apples were purchased at the local supermarket. Twenty apples per cultivar
were cut into pieces, put into falcon tubes and frozen in liquid nitrogen. The
frozen samples were stored at −80 ◦C until further sample preparation was
performed.
Four tomato cultivars (Lycopersicon esculentum Mill.) were selected for
this experiment: Aranca, Climaks, Clotilde and DRW 73-29. Twenty toma-
toes per cultivar were harvested at the Proefstation voor de Groenteteelt in
Sint-Katelijne-Waver (Belgium) at ripeness stage 5 (light red class) (USDA,
1975). The tomatoes were stored for one day at ambient atmosphere (18 ◦C
and 80% relative humidity). The day after harvesting the tomatoes were
cut into pieces, put into falcon tubes and frozen in liquid nitrogen. The
frozen samples were stored at −80 ◦C until further sample preparation was
performed.
Electronic tongue technology 49
Calibration curves for citric acid, malic acid and glucose where made
with the ETSPU, using the artificial juices with variable concentrations of
the compound of interest. The apple and tomato samples were analyzed
using the ETSPU with 27 sensors, ATR-FTIR (Chapter 4) and HPLC.
3.2.1.2 Comparison of two electronic tongues
Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based on
their difference in taste determined by a sensory panel, which is mainly de-
fined by the difference in sweetness and sourness, to assure a broad range in
acid and sugar content (Buysens, 2006a). The selected cultivars were: Ad-
miro, Macarena, Sunstream, Amoroso, Tricia and Clotilde (Table 3.2). The
fruit were obtained at the fruit- and vegetable Auction of Mechelen (Bel-
gium) and the Auction of Hoogstraten (Belgium). All tomatoes were picked
at ripeness stage 5 (light red class) (USDA, 1975). The fruit were stored
during one day at ambient atmosphere (18 ◦C and 80% relative humidity).
The day after purchase the tomatoes were juiced and the juice of the differ-
ent tomatoes was mixed all together in one large recipient of 10 L. The juice
was then divided over several falcon tubes and frozen in liquid nitrogen.
The samples were stored at −80 ◦C until measurement with the ETSPU,
the ASTREE ET, an enzyme based reference technique (EHT), atomic ab-
sorption spectroscopy (AAS), the optimized sequential injection-attenuated
total reflectance-Fourier transform infrared spectroscopy (SIA-ATR-FTIR)
system (Chapter 5) and sensory analysis (Chapter 6).
Table 3.2: Sweetness and sourness of the six Belgian tomato cultivars as perceived
by a sensory panel (+ high, 0 intermediate, - low).
Cultivar Sweetness Sourness
Admiro 0 0
Macarena - +
Sunstream + -
Amoroso + +
Tricia - -
Clotilde 0 0
50 3.2 Materials and methods
3.2.1.3 Quality control of fruit juices
Three multifruit juices of the brand Sunland (Sunnyland, Turnhout, Bel-
gium) were purchased at the local supermarket: Fruitdrink ACE, Benefits
Vitality and Benefits Immunity. The composition of the multifruit juices,
as mentioned on the label, is given in Table 3.3. The individual syrups
and juices used to compose the multifruit juices were provided by Sunny-
land Distribution (Turnhout, Belgium): blend-9 fruit (fixed blend made out
of pineapple, orange, passion fruit, mandarin, grapefruit, banana, mango,
guava and papaya), lemon, orange, passion fruit, apple, red grape, elder-
berry, cherry and strawberry. The juices and syrups were put in falcon
tubes, frozen using liquid nitrogen and stored at −80 ◦C until further sam-
ple treatment. Using the individual syrups 11 mixtures were made of which
three resembled the composition of the multifruit juices. Mixtures 1, 2 and
3 respectively correspond to Fruitdrink ACE, Benefits Vitality and Benefits
Immunity. The composition of all 11 mixtures is listed in Table 3.4. The
nine individual syrups, the three multifruit juices and the 11 mixtures were
analyzed in triplicate using the ETSPU and ATR-FTIR (Chapter 4).
3.2.2 Electronic tongues
3.2.2.1 Electronic tongue Saint-Petersburg University
The ETSPU consists of a sensor array of 18 to 27 potentiometric chem-
ical sensors. Non-specific sensors with both chalcogenide glass and PVC
plasticized membranes are comprised into the sensor arrays. The array con-
tains anionic sensors and cationic sensors, selected for their sensitivity to
organic acids and minerals, and a pH sensor. No information can be given
about the sensor composition due to a secrecy policy at the University of
Saint-Petersburg. Sensor potential values are measured versus a conven-
tional Ag/AgCl reference electrode with a precision of 0.1 mV. When the
sensors are immersed in a sample, within three minutes a potential over the
electrode membrane reaches an equilibrium value which is related to the
chemical composition of the sample. The potential values are recorded in
Electronic tongue technology 51
Table 3.3: Composition of three multifruit juices: Fruitdrink ACE, Benefits Vi-
tality and Benefits Immunity.
Fruitdrink ACE Benefits Vitality Benefits Immunity
Fruit
Blend-9 fruit 30% 11% 0%
Lemon 1% 0% 0%
Orange 0% 32% 0%
Passion fruit 0% 2.5% 0%
Apple 0% 53% 62.5%
Red grape 0% 0% 16%
Elderberry 0% 0% 9.5%
Cherry 0% 0% 2.5%
Strawberry 0% 0% 3.5%
Total fruit 31% 94% 94%
Extra compounds
Water yes no no
Aloe vera puree no yes yes
Provitam A yes no no
Fibers no yes yes
Vitamin C yes no no
Vitamin E yes yes yes
Minerals no yes yes
Aromas yes yes no
Sweetener yes no no
52 3.2 Materials and methods
Table
3.4
:C
omp
osit
ion
of
11
mix
ture
sm
ad
eou
tof
ind
ivid
ual
syru
ps
(%v/v).
Ble
nd-9
Lem
onO
rang
eP
assi
onA
pple
Red
Eld
erbe
rry
Che
rry
Stra
wbe
rry
frui
tfr
uit
grap
e
197
3
210
332.
554
365
1710
2.6
4
490
10
528
8.5
286
28
650
2010
20
766
33
866
1616
958
2227
83
1040
204
410
119
4523
23
Electronic tongue technology 53
data files. The ETSPU measurements were performed on apple and
tomato samples which were juiced using a mixer. Several measurements
were performed to determine the ideal sample preparation for ETSPU anal-
ysis. Using a fruit juice:water ratio of 1:5 makes it possible to detect a clear
sensor response and minimizes the amount of sample needed per assay. Ten
mL of juice was diluted with 50 mL of distilled water to reach a total vol-
ume which allowed all sensors to be immersed in the sample. In between the
measurements the sensors were rinsed using distilled water for seven min-
utes until stable sensor readings were recorded and the baseline potential
was reached again. After one and four minutes the distilled water was re-
newed. Two different sets of different sensors were used in the experiments
described in this thesis. A first set comprising 18 sensors was used in the
experiment dealing with both apple and tomato cultivars. A second set of
27 potentiometric sensors was applied in the experiment comparing two ET
systems and the experiment dealing with multifruit juices.
3.2.2.2 ASTREE electronic tongue Alpha M.O.S.
The ASTREE ET developed by Alpha M.O.S. (Toulouse, France) is com-
posed of seven liquid sensors. The commercially available set #1 (sensors ZZ,
BA, BB, CA, GA, HA and JB) was chosen for this particular experiment.
The sensors show selectivity to sugars, acids and minerals (AlphaM.O.S.,
2001a,b, 2006). Despite the fact that Alpha M.O.S. says that the sensors
give stable readings after one minute, the measurement time was set equal
to that of the ETSPU, i.e. three minutes. Following the guidelines of Alpha
M.O.S., the measurements were performed using 90 mL centrifuged tomato
juice. A total volume of 150 mL tomato juice was required to reach 90
mL centrifuged juice. Samples were centrifuged using a centrifuge (KR 22i
Jouan, Saint-Herblain Cedex, France) at 25000 g during five minutes. The
sample volume was not minimzed by dilution as with the ETSPU. In be-
tween measurements the sensors were rinsed using distilled water during 20
seconds, as Alpha M.O.S. prescribes. The sensors did not always reach their
baseline potential after this short cleaning period causing large drift in the
54 3.2 Materials and methods
measurement data. Despite this, data analysis was performed on absolute
data, as advised by the Alpha M.O.S. company, instead of on relative data.
3.2.3 Reference techniques
3.2.3.1 HPLC
The frozen apple and tomato samples were ground into a fine powder. The
grinding was partly performed by hand, using a mortar, and partly me-
chanically with a homogenizer (MM 200, Retsch, Haan, Germany). 0.1 g
of the frozen powder was transferred into a cooled 1.5 mL eppendorf tube
(Eppendorf, Hamburg, Germany) and again stored at −80 ◦C until the ex-
traction of the samples was performed. The organic acids and sugars were
extracted by adding 500 µL of 80% v/v ethanol/water to the frozen sam-
ples. After incubation in a temperature controlled shaker (Thermomixer
Comfort, Eppendorf, Hamburg, Germany) at 78 ◦C during 10 minutes, and
centrifugation at 4 ◦C and 20000 g during 5 minutes (Hawk 15/05 Refriger-
ated centrifuge, Sanyo, Bensenville, USA) the supernatant was transferred
in two new eppendorf tubes. For the analysis of organic acids 300 µL of
supernatant was used, for sugars 100 µL. Subsequently the eppendorfs with
the pellet were dry centrifuged (Concentrator 5301, Eppendorf, Hamburg,
Germany). For organic acid and sugar analysis, respectively 100 µL and 200
µL of HPLC water (Fisher Scientific, Loughborough, UK) was added to the
samples. After incubation, at the same conditions as mentioned before, the
samples were filtered on a 0.45 µm pore space filter (Alltech Associates Inc.,
Deerfield, USA).
The analysis of the organic acids and sugars were carried out on a Series
1100 HPLC (Agilent Technologies Inc., Palo Alto, USA). The acids were sep-
arated on a Prevail Organic Acid column (Alltech Associates Inc., Deerfield,
USA) at room temperature with a mobile phase of formic acid (pH = 2.5).
The organic acids were detected with a diode array detector (DAD) at 200
nm. The sugars were separated on an Aminex column (Bio-Rad, Hercules,
USA) with water as mobile phase and at a column temperature of 80 ◦C. The
Electronic tongue technology 55
sugars were detected with a refractive index detector (RID). Chemstation
software version 10.01 (Agilent technologies Inc., Palo Alto, USA) was used
to operate both HPLC systems and collect the chromatograms. Calibration
curves for malic acid, citric acid, sucrose, glucose and fructose were made.
Hereto individual chemical components were purchased at Sigma Aldrich
(Steinheim, Germany).
3.2.3.2 Enzymatic high throughput technique
An enzymatic high throughput method (EHT) was used as a reference
technique to evaluate sugar and acid content of the tomato samples in
the experiment comparing two ET’s. An automated liquid handling sys-
tem (Multiprobe II Plus, Perkin Elmer, Boston, USA) with four channels
was programmed to dispense all the reagents in the wells of the microtitre
plates. 96-well (NUNC, Roskilde, Denmark) and 384-well (Corning, New
York, USA) flat-bottomed non-treated polystyrene microtitre plates were
used. The absorbances at the specified wavelengths were read with a Multi-
skan Spectrum (Thermo Electron Corporation, Waltham, USA). The enzy-
matic assays for the analysis of glucose, fructose, citric acid, malic acid and
glutamate were purchased from R-Biopharm (Darmstadt, Germany). The
assays are based on an increase/decrease in absorbance at specific wave-
lengths caused by a change in NAD(P)H (340 nm). The absorbance of the
chromogenic molecules is measured before and after the addition of the sub-
strate specific enzyme and is corrected for the delta absorbance of the blank
values. The tomato samples were filtered using a 0.45 µm pore filter (All-
tech Associates Inc., Deerfield, USA) preceding the analysis. All samples
were analyzed in duplicate together with a calibration curve, consisting of
four points with three repetitions per concentration, on the same microtitre
plate. All compounds were purchased at Sigma Aldrich (Steinheim, Ger-
many). Since the concentrations of the acids and sugars in the samples were
too high to be analyzed directly, dilution with distilled water was necessary
to obtain concentrations that were in the linear range of the calibration
curve. A detailed description is given by Vermeir et al. (2007). Four repe-
56 3.2 Materials and methods
titions of each cultivar were analyzed using this fast reference technique.
3.2.3.3 Atomic absorption spectroscopy
For the analysis of minerals in the tomato samples, atomic absorption spec-
troscopy (AAS) was applied as a reference technique. The concentrations of
Na and K, which have an influence on the saltiness of the tomato samples,
were determined using a flame atomic absorption spectrometer (Solaar 969
A Thermo Elemental, Cambridge, U.K.). The tomato samples were filtered
using a 0.45 µm pore filter.
3.2.4 Statistical analysis
The multidimensional signals of the electronic tongues required data pre-
treatment before statistical analysis could be performed. Both the ETSPU
and the ASTREE ET comprised potentiometric sensors of which some were
sensitive to drift during the experiment. The drifting sensors were deleted
from the sensor array based on the sensor stability. A coefficient of varia-
tion (CV value) was calculated for each sensor and cultivar. The CV value
is defined as the standard deviation divided by the mean, multiplied by 100
percent. Sensors with an average CV value of more than 10 were considered
as unstable during the experiment.
Multivariate data analysis was applied for both qualitative and quan-
titative analysis using both multisensor systems. Partial least squares-
discriminant analysis (PLS-DA) was used for data visualization and clus-
tering of observations in the data structure. Using this technique, intra-
cultivar effects are minimized and inter-cultivar effects are maximized. The
analysis was performed on the covariance matrix. Outliers were deleted from
the analysis based on their scores, leverages (distance to the model centre
for each object summarized over all components) and residuals (Geladi and
Dabakk, 1995). The results of the PLS-DA performed on the ET data were
compared to those of the reference techniques.
Electronic tongue technology 57
Principal component analysis (PCA), an unsupervised method, was used
as a data exploration technique on the data of the fruit juices, syrups and
mixtures. The potential of the ETSPU as a tool for quality control was
examined using this technique. The multifruit juices, syrups and their mix-
tures were grouped using PCA (Johnson and Wichern, 1992).
Partial least squares analysis (PLS) was performed to study the pre-
dictive capacity of the ET’s for individual compounds and syrups. The
calibration models were validated using cross-validation, where a small set
of randomly selected samples is left out of to construct the model and is
used afterwards for validation purposes. PLS2 was used for the prediction
of the sugars, acids and minerals in apples and tomatoes and the prediction
of syrups in multifruit juices. In a PLS2 analysis, all compounds of interest
(Y variables) are related together to the sensor readings (X variables). The
results of the HPLC or EHT and AAS measurements were taken as ref-
erences for the assessment of individual chemical compounds in apple and
tomato. For the prediction of individual compounds in the artificial juice,
PLS1 was used. In this case, the concentration of the glucose, citric acid
and malic acid are related one by one (one Y variable) to the sensor read-
ings (X variables) since each time only one compound was varied in the
artificial juices (Martens and Naes, 1998). Prediction models having a cor-
relation above 0.90, high slope and low offset were considered to be good.
The correlation gives information about the quality of the model, but gives
no direct information about the prediction accuracy. The RPD value is a
factor which indicates the accuracy. An RPD value between 2 and 2.5 makes
approximate quantitative predictions possible. For values between 2.5 and
3, and above 3, the prediction is classified as good and excellent, respectively
(Saeys et al., 2005). For data analysis two different computer software pro-
grams were used: The Unscrambler version 9.1.2 (CAMO Technologies Inc.,
Oslo, Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).
58 3.3 Results
3.3 Results
3.3.1 Classification of apple and tomato cultivars and quan-
tification of taste compounds
3.3.1.1 Classification with HPLC
Differences in the concentrations of all individual compounds between the
cultivars are studied looking at the average values. The results of the HPLC
data of both the apple and tomato samples are shown in Table 3.5. The
apple samples contain high concentrations of fructose. While Pinova has the
highest concentration of this sugar, this cultivar has a low concentration of
sucrose. Fructose, sucrose and malic acid are the most abundant chemical
taste compounds in apple. The differences in the content of sucrose and
malic acid between the different cultivars are large. Cox and Golden contain
a higher concentration of sucrose than the other three cultivars. The apple
cultivar Cox shows a higher content of sucrose and malic acid than most of
the other cultivars.
Tomatoes, on the other hand, contain high concentrations of citric acid.
The highest concentration of this acid is found with Climaks and Clotilde.
Glucose and fructose are the two most abundant sugars in the tomato sam-
ples. Aranca contains higher glucose and fructose contents and lower citric
acid and malic acid concentrations than the other three cultivars. This
tomato cultivar is a cherry tomato which is known for its sweet taste.
After a first screening of the data, the results of the reference measure-
ments of all compounds were jointly analyzed using multivariate data anal-
ysis. The possibility of HPLC to group samples of one cultivar and, thus,
to classify cultivars based on their content of taste compounds is studied
using PLS-DA. Figures 3.1 and 3.2 show the PLS-DA results of respectively
the apple and tomato measurements performed by HPLC as reference tech-
nique. Elstar is positioned in the center of the score plot, while the other
four cultivars each are placed in one of the quadrants. The within cultivar
variance is caused by biological variability and measurement error. Along
Electronic tongue technology 59
Table 3.5: Average results of HPLC measurements performed on apple and tomato
samples (average ± standard deviation, concentrations in mg/g powder).
Cultivar Sucrose Glucose Fructose Malic acid Citric acid
Apple
Cox 26±3 3.8±0.6 24±2 12±2 0.26±0.05
Elstar 12±2 7±1 21±3 10±2 0.21±0.03
Golden 21±3 6±1 26±5 12±2 0.16±0.04
Jonagold 12±2 8±2 28±4 7±1 0.15±0.04
Pinova 11±2 8.9±1 29±4 8±1 0.21±0.04
Tomato
Aranca 8.4±0.7 22±2 20±2 0.10±0.04 11±1
Climaks 6±1 12±3 13±3 1.4±0.3 18±4
Clotilde 4±2 17±2 15±1 0.9±0.1 18±3
DRW 73-29 4.1±0.7 13±2 12±1 1.3±0.2 15±2
the axis of the first principal component (PC) apple cultivar Cox is clearly
separated from the other cultivars. The trend which is visible along the axis
of the first PC is related to the malic acid, sucrose and glucose content of
the samples. The order in which the apple cultivars appear along this axis,
is the same trend that is found in the malic acid content (Table 3.5) going
from Cox over Golden and Elstar to Jonagold and Pinova. In the correla-
tion loadings plot two ellipses are visible. The inner and outer ellipses on
the figures represent correlation coefficients (R) of 70% and 100% (or R2
values of 50% and 100%). For a taste compound located between the two
ellipses more than 70% of its variability is explained by the first two prin-
cipal components. This means this variable is important in describing the
variability, and, hence, the major cultivar effects, within the data set. The
correlation loadings plot shows that Cox is highly related to both malic acid
and sucrose. Golden and Jonagold can be separated, though not completely,
from the other cultivars in the direction of the second PC. The trend along
this axis is mainly caused by citric acid. The HPLC results show indeed
that Pinova, Elstar and Cox have a higher content of these compounds than
Golden and Jonagold. Projected in the two dimensional space of the first
two PC’s, Jonagold is correlated to fructose.
60 3.3 Results
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3 4
PC
2
PC 1
CoxElstarGoldenJonagoldPinova
A
Malic acid
Citric acid
GlucoseCox
ElstarPinova
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1Malic acid
SucroseFructose
Golden
Jonagold
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
B
Figure 3.1: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
apple samples measured by HPLC (X-expl. (52%, 16%); Y-expl. (21%, 15%)).
Electronic tongue technology 61
-2
-1
0
1
2
-4 -3 -2 -1 0 1 2 3
PC
2
PC 1
ArancaClimaksClotildeDRW 73-29
A
Malic acid
Citric acid
Sucrose
Glucose
Fructose
Aranca
Climaks
Clotilde
DRW 73-29
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC
2
PC 1
B
Figure 3.2: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
tomato samples measured by HPLC (X-expl. (74%, 10%); Y-expl. (31%, 21%)).
62 3.3 Results
The tomato results (Figure 3.2) show a separation between the cultivars
based on the HPLC results. The within cultivar variance, here too, is mainly
caused by biological variability. Aranca is clearly separated from the other
tomato cultivars. This separation is caused based on the glucose, fructose
and sucrose content of the cherry tomato. In the correlation loadings plot
Aranca is highly correlated with these three sugars. The concentrations
found in Aranca are different from the other cultivars (Table 3.5). In the
direction of the second PC Climaks can be classified separated from Clotilde
and DRW 73-29. This cultivar, however, can not be correlated highly to one
of the chemical compounds which were analyzed.
3.3.1.2 Classification with ETSPU
The ETSPU used in this experiment is comprised of 27 potentiometric sen-
sors of which some were sensitive to drift during the experiment with apples
and tomatoes. The drifting sensors were deleted from the sensor array in
each experiment separately based on the sensor stability. The CV values of
all sensors of the ETSPU system are shown in Table 3.6 for apple and Table
3.7 for tomato. Sensors with a CV value of more than 10 were considered
as unstable during the experiment. In the experiment with the apples, five
sensors were deleted from the data matrix. In the tomato experiment, 21
sensors were retained for further data analysis. Four of the discarded sensors
are the same for both apple and tomato. At this moment, however, should
be stated that the sensors were not deleted from the data matrix based only
on their instability. Since one fruit was taken as one sample, biological vari-
ability is also included in the experiment. This biological variability could
also have caused a variation in the data which is recognized as sensor in-
stability. Since no changes could be made to the experiment, the possible
effect of biological variability was ignored.
The ETSPU data from apple and tomato were jointly analyzed with PLS-
DA. Apple and tomato are clearly separated from each other in the score
plot (Figure 3.3). All five apple cultivars are positioned at the negative end
of the first PC, while the four tomato cultivars are located at the positive
Electronic tongue technology 63
Table 3.6: CV values of the sensors of the ETSPU (apples).
Sensor CV value Sensor CV value
1 2.5 16 112
2 7.4 17 3.7
4 4.0 18 5.7
5 24 19 4.7
6 3.0 20 11
7 2.8 21 1.5
8 2.2 22 8.4
9 2.9 23 7.6
10 7.3 24 6.3
11 6.2 25 24
12 1.7 26 12
13 4.3 27 7.0
14 3.8 28 2.3
15 4.2
Table 3.7: CV values of the sensors of the ETSPU (tomatoes).
Sensor CV value Sensor CV value
1 3.3 16 100
2 4.4 17 5.5
4 4.1 18 8.0
5 54 19 5.4
6 7.0 20 11
7 5.8 21 1.8
8 6.0 22 7.9
9 3.4 23 7.0
10 5.4 24 13.8
11 5.1 25 2.4
12 1.4 26 20
13 3.5 27 19
14 3.3 28 2.4
15 6.8
64 3.3 Results
-80
-60
-40
-20
0
20
40
60
-200 -150 -100 -50 0 50 100 150 200
PC
2
PC 1
Apple Tomato
Figure 3.3: Score plot of the PLS-DA of the apple and tomato samples measured
using the ETSPU (X-expl. (72%, 16%); Y-expl. (82%, 12%)).
end. The correlation loadings plot (not shown) indicates that apple is highly
correlated with malic acid, sucrose and fructose and sensors 1, 4, 6, 7, 8,
9, 12, 19, 21, 25 and 28. Tomato is correlated with citric acid, glucose and
sensors 14 and 15. Table 3.5 lists that apple indeed contains more malic
acid, sucrose and fructose than tomato, which contains more citric acid and
glucose. The correlation loadings plot indicates a relation between malic
acid, sucrose and fructose and 11 sensors of the ETSPU. Citric acid and
glucose are moderately related with sensors 14 and 15.
The results of the PLS-DA performed on the ETSPU readings of the ap-
ple samples are shown in Figure 3.4. Pinova is positioned slightly separated
from the other apple cultivars. Based on the correlation loadings of this
analysis (not shown), however, no sensors are appointed as being responsi-
ble for this classification. Also, no explanation is found based on the sugar
Electronic tongue technology 65
and acid content as measured by HPLC. The reason for the separation of
Pinova from the other four apple cultivars might be caused by differences
in mineral content. Minerals were, however, not analyzed in this experi-
ment. The spreading of the samples within one cultivar is larger using the
ETSPU, compared to the reference analysis (Figure 3.1). The classification
results of both the reference technique and ET are presented in Table 3.8. A
large difference is present between HPLC and ETSPU. Using the reference
technique, more samples are classified correctly.
-6
-4
-2
0
2
4
6
-6 -4 -2 0 2 4 6 8
PC
2
PC 1
CoxElstarGoldenJonagoldPinova
Figure 3.4: Score plot of the PLS-DA of the apple samples measured using the
ETSPU (X-expl. (42%, 20%); Y-expl. (17%, 10%)).
Figure 3.5 shows the results of the same analysis performed on the
tomato data. Here too, the within cultivar variance in larger than in the
analysis performed on the HPLC data (Figure 3.2), especially for Aranca.
Aranca is separated from the other tomato cultivars. This tomato cultivar
is, as indicated by the correlation loadings plot (not shown), correlated to
66 3.3 Results
sensors 1, 12, 19 and 21 of the ETSPU. This indicates that these sensors are
related to the three sugars present in the samples, which could also be seen
in previous analysis of apples and tomatoes together (Figure 3.3), where
these sensors are related to sucrose and fructose. The other three cultivars
are not correlated to any of the sensors in the space created by the first two
PC’s. Table 3.8 shows that, as with the apples, the percentage of correct
classified samples is less with the ETSPU than with HPLC.
2
4
6
PC
2
PC 1
ArancaClimaksClotildeDRW 73-29
-6
-4
-2
0-8 -6 -4 -2 0 2 4 6 8 10
Figure 3.5: Score plot of the PLS-DA of the tomato samples measured using the
ETSPU (X-expl. (43%, 13%); Y-expl. (15%, 10%)).
Electronic tongue technology 67
Table 3.8: Classification results of the PLS-DA performed on the reference data
and ETSPU measurements on apple and tomato. The percentage of correct classi-
fied samples is shown (%).
Cultivar HPLC ETSPU
Cox 100 25
Elstar 75 75
Jonagold 85 0
Golden 35 25
Pinova 60 90
Aranca 100 85
Climaks 90 60
Clotilde 65 15
DRW 73-29 85 10
3.3.1.3 Quantification with ETSPU
Next to classification, the potential of the ETSPU to quantify chemical
components was studied in a PLS analysis. Since the ETSPU gives potential
readings, the logarithm of the concentration of all compounds is used for the
analysis. Using the model solutions, resembling artificial fruit juices, PLS
models were built for each of the compounds of interest. Since different
mixtures were made for each compound, PLS1 regression was used. The
results of the prediction models for glucose, malic acid and citric acid are
shown in Table 3.9. The calibration models for citric acid and malic acid
have high correlations between the measured and predicted concentrations.
The correlations between the measured and predicted concentration in
the validation models of both acids are very high too, with values of respec-
tively 99% and 98% for malic acid and citric acid. In addition, both the
RMSEC and RMSECV values are low. Despite the good results for citric
acid, a large difference is present between the slopes of the calibration and
validation model. With a slope of 85%, the predicted values of citric acid
are an underestimation of the observed values. The models for glucose are
not satisfactory. The validation model is very different from the calibration
68 3.3 Results
model with a lower correlation and a higher RMSECV value. The corre-
lation between the measured and predicted concentration of glucose in the
validation model is only 83%. The high RMSE values indicate that the cal-
ibration model of glucose is better than the validation model. The ETSPU,
however, has an excellent accuracy to predict the concentrations of citric
acid and malic acid in a chemical solution, with high correlations and RPD
values of respectively 4.4 and 7.2. Since the ETSPU is a potentiometric
device, ions are measured. This explains why the prediction of citric acid
and malic acid is better than that of glucose.
Table 3.9: PLS1 models based on the ETSPU readings of artificial juices (log-
arithm of concentration). Cross-validation was used to validate the model. The
offsets, RMSEC and RMSECV values are given in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Glucose Calibration 0.95 0.00 0.98 0.04
Validation 0.97 0.09 0.83 0.16 1.5
Malic acid Calibration 0.99 0.00 0.99 0.04
Validation 0.94 0.01 0.99 0.15 7.2
Citric acid Calibration 0.98 0.02 0.99 0.05
Validation 0.85 0.18 0.98 0.08 4.4
A PLS2 regression was performed to predict the concentration of the
taste compounds of apple and tomato. The results of the analysis for apple
and tomato are shown in respectively Table 3.10 and 3.11. The calibration
and validation models of both apple and tomato samples do not show any
possibility of the ETSPU to predict the chemical composition of the sam-
ples. All correlations are too low to talk about a good correlation between
instrumental techniques. Both for apple and tomato, the ’best’ PLS model
is made for malic acid. The correlations of the calibration models are re-
spectively 80% and 89% for apple and tomato. The offsets of the models
made for malic acid are close to zero. The slopes, however, are low and the
RMSEC values, which are a measure for the prediction error of the model,
are high. The validation models of malic acid are also less good with cor-
Electronic tongue technology 69
relations of 74% for apple and 84% for tomato and high RMSECV values.
The RPD values are all below 2, indicating that the made models can not
be used to predict the chemical composition of the samples.
Table 3.10: PLS2 models based on the ETSPU readings of apples (logarithm
of concentration) Cross-validation was used to validate the model. The offsets,
RMSEC and RMSECV values are given in mg/g.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Malic acid Calibration 0.64 -0.11 0.80 0.15
Validation 0.61 -0.12 0.74 0.17 0.7
Citric acid Calibration 0.28 -2.25 0.53 0.23
Validation 0.20 -2.49 0.37 0.25 0.5
Sucrose Calibration 0.66 0.40 0.81 0.10
Validation 0.61 0.47 0.75 0.11 1.6
Glucose Calibration 0.57 0.35 0.75 0.10
Validation 0.49 0.41 0.66 0.12 1.3
Fructose Calibration 0.41 0.82 0.64 0.06
Validation 0.35 0.91 0.55 0.07 1.2
Table 3.11: PLS2 models based on the ETSPU readings of tomatoes (logarithm
of concentration). Cross-validation was used to validate the model. The offsets,
RMSEC and RMSECV values are given in mg/g
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Malic acid Calibration 0.80 -0.04 0.89 0.22
Validation 0.74 -0.05 0.84 0.26 1.9
Citric acid Calibration 0.54 0.55 0.73 0.07
Validation 0.46 0.64 0.62 0.09 1.4
Sucrose Calibration 0.56 0.32 0.75 0.10
Validation 0.49 0.38 0.66 0.12 1.4
Glucose Calibration 0.51 0.58 0.71 0.08
Validation 0.41 0.69 0.59 0.09 1.6
Fructose Calibration 0.45 0.65 0.67 0.07
Validation 0.35 0.76 0.54 0.08 1.5
70 3.3 Results
3.3.2 Comparison of two electronic tongues
3.3.2.1 Classification with reference techniques EHT and AAS
The average concentrations of carbohydrates, organic acids and minerals
measured by the reference techniques, EHT and AAS, are shown in Table
3.12. Amoroso, a cherry cluster tomato, has high concentrations of both
sugars, citric acid, glutamate and both measured minerals and a low con-
centration of malic acid. Due to its chemical content this is a very tasty
tomato with a specific sweet and sour taste. Sunstream, another cocktail
cluster tomato, also shows high concentrations of both sugars, but lower than
Amoroso. Tricia has low concentrations of all compounds. This cultivar is
known for its unpronounced taste (Buysens, 2006a). Admiro, Macarena
and Clotilde all have intermediate concentrations of sugars and acids, they,
however, have different tastes (Table 3.2).
In the PLS-DA performed on the data of both reference techniques, EHT
and AAS, the six tomato cultivars are clearly separated from each other
(Figure 3.6). Amoroso, the cherry cluster tomato, is clearly separated from
the other cultivars. The axis of the first PC can be seen as a sugar axis, going
from Amoroso with a high sugar content to Tricia with low concentrations
of all sugars. Admiro and Tricia cannot be separated from each other along
the axis of the first PC, which is related to the sugar and glutamate content.
Table 3.12 shows that these two cultivars do not differ a lot in their fructose
and glutamate content. The concentrations of citric and malic acid however
are different in both tomato cultivars. Thus, the separation along the axis of
the second PC is caused by this difference in acid concentration. Negative
scores for PC2 correspond to high concentrations of citric acid and malic
acid. Despite the fact that Admiro and Macarena have a very different
chemical composition, they are close to each other in the score plot of the
PLS-DA.
Electronic tongue technology 71
-2
-1
0
1
2
-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8
PC
2
PC 1
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
A
Glucose
Citric acid
Malic acid
Admiro
Macarena
Sunstream
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1Fructose
GlutamateAmoroso
Tricia
Clotilde
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Figure 3.6: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
tomato samples measured by EHT and AAS (X-expl. (74%, 16%); Y-expl. (19%,
19%)).
72 3.3 Results
Table
3.1
2:
Ave
rage
resu
lts
of
EH
Tan
dA
AS
mea
sure
men
tsp
erfo
rmed
on
tom
ato
sam
ple
s(a
vera
ge±
stan
dard
dev
iati
on
,co
nce
ntr
ati
on
sin
g/L
juic
e).
Cul
tiva
rG
luco
seFr
ucto
seC
itri
cM
alic
Glu
tam
ate
Na
K
acid
acid
Adm
iro
11.2±
0.3
11.3±
0.3
4.8±
0.5
0.72±
0.01
0.95±
0.03
9.2±
0.2
1.8±
0.2
Mac
aren
a15
.6±
0.2
14.4±
0.2
4.3±
0.5
0.88±
0.02
0.64±
0.02
7.4±
0.4
1.9±
0.3
Suns
trea
m17
.7±
0.4
17.4±
0.3
5.4±
0.4
0.51±
0.01
1.45±
0.02
7.9±
0.6
2.2±
0.3
Am
oros
o23
.7±
0.3
23.5±
0.5
5.8±
0.3
0.27±
0.01
2.82±
0.08
82.9±
4.1
2.7±
0.1
Tri
cia
10.0±
0.1
10.8±
0.2
2.9±
0.4
0.51±
0.01
0.88±
0.03
8.8±
0.6
1.6±
0.1
Clo
tild
e13
.3±
0.2
13.4±
0.2
3.7±
0.3
0.55±
0.01
1.07±
0.01
46.0±
3.4
1.77±
0.04
Electronic tongue technology 73
3.3.2.2 Classification with ETSPU and ASTREE ET
The multidimensional signals of both ET’s required data pretreatment be-
fore statistical analysis could be performed. Drifting sensors were deleted
from both sensor arrays based on the sensor stability. The CV values of
all sensors of both ET systems are shown in Table 3.13 and 3.14. Since
no biological variability was present in this experiment, it can be concluded
that high CV values represent sensor instability. Sensors with a CV value
of more than 10 were considered as unstable during the experiment. Four-
teen sensors out of 18 of the ETSPU were retained for further data analysis.
The extreme CV values found for sensor 12 will probably be due to arte-
facts. One sensor, sensor JB, was deleted from the ASTREE sensor array
for analysis.
Table 3.13: CV values of the sensors of the ETSPU.
Sensor CV value Sensor CV value
1 2.2 10 3.5
2 3.5 11 7.2
3 3.8 12 2931
4 4.3 13 13
5 2.5 14 12
6 1.4 15 2.0
7 1.5 16 1.3
8 2.7 17 2.2
9 3.7 18 774
Figure 3.7 shows the results of the PLS-DA on the data of the ET-
SPU. Along the axis of the first PC three groups of cultivars are visible.
Amoroso on the negative side of this PC is separated from the other cul-
tivars. Macarena and Tricia are positioned at the positive end of the axis,
while Admiro, Sunstream and Clotilde are located in the center of the score
plot. This clustering is probably caused by the difference in the content of
both sugars and minerals as measured by the reference techniques (Table
3.12). In the correlation loadings plot, Amoroso is correlated with four sen-
74 3.3 Results
Table 3.14: CV values of the sensors of the ASTREE ET developed by Alpha
M.O.S.
Sensor CV value
ZZ 3.6
BA 4.0
BB 0.6
CA 1.8
GA 4.5
HA 3.8
JB 38
sors: sensors 8, 9, 10 and 11. Because of the high sugar and mineral content
of this cultivar, these sensors might be related to the sugars and minerals
present in the samples. Macarena and Tricia are somewhat correlated with
respectively sensors 4 and 15 and sensors 3 and 7. The percentage of correct
classified samples of the ETSPU is shown in Table 3.15. Comparing the re-
sults of the reference techniques to those of the ETSPU, it can be concluded
that the multisensor system is not able to classify the samples as good as
the reference techniques. None of the samples of Macarena and Sunstream
are classified correctly.
Table 3.15: Classification results of the PLS-DA performed on the reference data,
ETSPU and ASTREE ET measurements. The percentage of correct classified sam-
ples is shown (%).
Cultivar EHT/AAS ETSPU ASTREE ET
Admiro 80 67 100
Macarena 100 0 100
Sunstream 100 0 90
Amoroso 100 67 100
Tricia 100 25 90
Clotilde 100 17 90
Electronic tongue technology 75
-4
-2
0
2
4
-8 -6 -4 -2 0 2 4 6
PC
2
PC 1
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
A
S1
S2
S3
S5
S7S9
S17
Admiro
Sunstream
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
S4
S6
S8S9
S10S11
S15
S16
Macarena
Amoroso TriciaClotilde
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
B
Figure 3.7: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
tomato samples measured using the ETSPU (X-expl. (58%, 22%); Y-expl. (18%,
8%)).
76 3.3 Results
-4
-3
-2
-1
0
1
2
3
-4 -3 -2 -1 0 1 2 3
PC
2
PC 1
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
A
Sensor ZZSensor BA
Sensor BB
Sensor CA
Sensor GA
Sensor HA
MacarenaTricia
Clotilde
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
Admiro
SunstreamAmoroso
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
B
Figure 3.8: Score plot (A) and correlation loadings plot (B) of the PLS-DA of
the tomato samples measured using the ASTREE ET developed by Alpha M.O.S.
(X-expl. (45%, 33%); Y-expl. (14%, 7%)).
Electronic tongue technology 77
The results of the PLS-DA performed on the data resulting from the
ASTREE ET, after exclusion of sensor JB from the dataset, are shown in
Figure 3.8. Two groups are found along the axis of the first PC. Amoroso
and Clotilde are separated from the other four cultivars. The clustering of
these two cultivars, however, cannot be explained by their chemical composi-
tion, since both cultivars have very different concentrations and proportions
of sugars, acids and minerals (Table 3.12). In the direction of the second
PC Admiro is classified separated from the other cultivars. No important
correlations between the tomato cultivars and the sensors are noticed in the
correlation loadings plot. The ASTREE ET clearly contains different in-
formation than the reference techniques. The multisensor system, however,
is able to classify most of the samples within the correct cultivar (Table
3.15). Compared to the results of the ETSPU, the ASTREE ET is better
in classifying the samples by cultivar.
3.3.2.3 Quantification with ETSPU and ASTREE ET
After studying the ability of both ET’s to classify tomato cultivars, the
potential of both multisensor systems to quantify the chemical content of the
samples was examined. In PLS2 models the sensor readings were coupled to
the results of the reference measurements. The results of the PLS2 analyses
are shown in Table 3.16 and Table 3.17. Since the ETSPU gives potential
readings, the logarithm of the concentration of all compounds is used for
the analysis. As shown in Table 3.16, the ETSPU is able to predict the
concentration of all sugars, acids and minerals of interest. The PLS2 models
show slopes close to one and low offsets. All slopes are higher than 90%,
except for the validation model of citric acid, and all offsets are close to zero.
The correlations between measured and predicted values of all PLS2 models
are high and close to one. The RMSECV values, which are a measure
for the prediction error, are very low. The fact that the calibration and
validation models are very similar to each other is another proof of the
reliability of these models. The high RPD values indicate that the ETSPU
is highly suited to determine glucose, fructose, malic acid, glutamate and
78 3.3 Results
Na. The RPD values of the models predicting citric acid and K are between
2 and 2.50, indicating that approximate predictions of these compounds are
possible using the ETSPU.
Table 3.16: PLS2 models based on the ETSPU readings of tomatoes (logarithm
of concentration). Cross-validation was used to validate the model. The offsets,
RMSEC and RMSECV values are given in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Glucose Calibration 0.96 0.04 0.98 0.02
Validation 0.93 0.08 0.95 0.04 3.2
Fructose Calibration 0.98 0.03 0.99 0.02
Validation 0.95 0.05 0.96 0.03 3.9
Citric acid Calibration 0.92 0.05 0.96 0.03
Validation 0.84 0.10 0.87 0.05 2.3
Malic acid Calibration 0.97 -0.01 0.98 0.03
Validation 0.94 -0.02 0.96 0.05 3.2
Glutamate Calibration 0.96 0.003 0.98 0.04
Validation 0.94 0.002 0.95 0.06 3.5
Na Calibration 0.99 0.01 0.99 0.04
Validation 0.92 0.10 0.97 0.10 4.1
K Calibration 0.95 0.16 0.97 0.02
Validation 0.91 0.30 0.91 0.04 2.3
The PLS2 models based on the measurements of the ASTREE ET are
listed in Table 3.17. The results are very different from those of the ETSPU.
In case of the ASTREE ET the concentrations of the individual compounds
were used in the model as prescribed by the Alpha M.O.S. company. A log-
arithmic transformation was tried in the analysis, but the most satisfactory
results were found using raw data. All compounds show PLS2 prediction
models which are not satisfactory. The slopes and offsets of both calibration
and validation models are not acceptable. All slopes are low and the offsets
of glucose, fructose and, especially, K are far from zero. The correlations
between measured and predicted values of both the calibration and valida-
tion models of glutamate and Na are acceptable, ranging between 80% and
Electronic tongue technology 79
94%. The models built for the other sugars, acids and K show correlations
that are not sufficient to ensure good predictions. The RMSECV values of
all models are in line with the slope, offset and correlation, showing high
values for glucose, fructose and K, but also for Na. All RPD values are very
low, indicating that the models are not good. From these results can be
stated that the ASTREE ET equipped with this set of sensors is not able
to quantify individual chemical compounds in tomato juices.
Table 3.17: PLS2 models based on the ASTREE ET readings of tomatoes. Cross-
validation was used to validate the model. The offsets, RMSEC and RMSECV
values are given in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Glucose Calibration 0.52 7.17 0.72 2.98
Validation 0.36 9.73 0.49 3.90 0.03
Fructose Calibration 0.61 5.81 0.78 2.53
Validation 0.47 8.05 0.56 3.52 0.03
Citric acid Calibration 0.65 1.58 0.80 0.64
Validation 0.54 2.15 0.62 0.89 0.1
Malic acid Calibration 0.70 0.17 0.84 0.10
Validation 0.58 0.25 0.72 0.12 1.4
Glutamate Calibration 0.81 0.24 0.90 0.29
Validation 0.74 0.33 0.80 0.41 0.5
Na Calibration 0.87 3.12 0.94 9.53
Validation 0.80 5.56 0.85 15 0.03
K Calibration 0.54 910 0.74 270
Validation 0.37 1255 0.52 348 0.0003
3.3.3 Quality control of fruit juices
The ETSPU with a sensor array of 18 potentiometric sensors was used for
the measurement of fruit juice compositions. The same set of sensors was
used as in the previous experiment. None of the sensors were sensitive to
drift during the experiment. The CV values of all sensors of the ETSPU
system are shown in Table 3.18. After the previous experiment dealing with
80 3.3 Results
tomato samples, sensor 12 was replaced by a new sensor. All sensors were
included in the data analysis.
Table 3.18: CV values of ET sensors.
Sensor CV value Sensor CV value
1 0.81 10 1.1
2 1.4 11 2.4
3 1.4 12 7.5
4 2.6 13 2.1
5 0.83 14 2.9
6 0.81 15 1.7
7 0.79 16 0.76
8 0.63 17 0.48
9 0.96 18 2.7
-20
-15
-10
-5
0
5
10
15
20
-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30
PC
2
PC 1
Ace
Benefits Vitality
Benefits Immunity
Figure 3.9: Score plot of the PCA of the three multifruit juices measured using
the ETSPU (X-expl. (72%, 25%)).
Electronic tongue technology 81
A PCA was performed on the data of the ETSPU measurements of the
different fruit juice samples to explore the data. The analysis of the three
multifruit juices is shown in Figure 3.9. The three juices are clearly separated
from each other in the score plot. 97% of the variance is explained by the
first two PC’s. According to the correlation loadings (not shown), the first
PC is characterized by anionic sensors 1, 2, 3, 5, 6, 7 and 14, cationic sensors
9, 10, 11 and 17 and sensor 18, the pH sensor.
Figure 3.10 shows the results of the PCA on the data of the nine syrups
and mixtures. The syrups provided by Sunnyland and their mixtures are
grouped in a PCA using the ETSPU data. 80% of the variance is ex-
plained by the first two PC’s. Blend-9 fruit, orange, lemon and passion
fruit syrup are grouped together. An explanation, except for lemon syrup,
can be found in the composition of the blend-9 fruit syrup. This syrup is
made out of pineapple, orange, passion fruit, mandarin, grapefruit, banana,
mango, guava and papaya. Both orange and passion fruit are present in
high concentrations in the blend-9 fruit syrup. The close classification with
lemon syrup is probably caused by the similarities in the chemical compo-
sition of orange and lemon juice. Also, all four syrups are present in many
of the mixtures. Mixtures 1, 2, 4, 5, 6 and 7 are grouped together with the
four syrups. All of these mixtures contain high concentrations of the four
syrups. Mixture 8 is positioned in between orange syrup and apple syrup.
This mixture contains a high content of these two syrups. Red grape, el-
derberry, cherry and strawberry are grouped in two distinctive groups. In
between both groups, the mixtures which are prepared out of the four syrups
are positioned. Mixture 3, which resembles Benefits Immunity, has a fruit
composition similar to that of mixtures 9, 10 and 11, which explains its clas-
sification in the first quadrant of the score plot in between these mixtures.
The separation of red grape and strawberry from the other syrups along the
axis of the first PC is mainly caused by anionic sensors 3, 6, 14 and 16 and
cationic sensors 10 and 11.
82 3.3 Results
-100
-50
0
50
100
150
-100 -50 0 50 100 150 200P
C 2
PC 1
Blend-9 fruit Lemon OrangePassion fruit Apple Red grapeElderberry Cherry StrawberryMix 1 Mix 2 Mix 3Mix 4 Mix 5 Mix 6Mix 7 Mix 8 Mix 9Mix 10 Mix 11
Figure 3.10: Score plot of the PCA of the 9 syrups and 11 mixtures measured
using the ETSPU (X-expl. (59%, 21%)).
Finally, the three mixtures that were made based on information about
the fruit content of the three multifruit juices were analyzed together with
the three multifruit juices (Figure 3.11). 97% of the variance in the data is
explained using two PC’s. The three multifruit juices are clearly separated
from the three mixtures. All sensors, except for sensors 4, 8, 12, 13 and
18, seem responsible from this grouping along the axis of the first PC. The
separation is based on the difference in the composition of the samples. Since
the fruit content is the same in both the multifruit juice and the mixture
related with it, the separation is caused by the presence of other compounds,
like the vitamins, aloe vera puree and minerals (Table 3.3). Information and
ETSPU measurements of these extra components present in the multifruit
juices are thus necessary to make a good classification model based on the
fruit content.
Electronic tongue technology 83
-80
-60
-40
-20
0
20
40
60
80
100
-150 -100 -50 0 50 100 150 200
PC
2
PC 1Ace
Benefits Immunity Benefits
Vitality
Mix Ace
Mix Benefits Immunity
Mix Benefits Vitality
Figure 3.11: Score plot of the PCA of the three multifruit juices and the mixtures
with the same fruit content measured using the ETSPU (X-expl. (91%, 7%)).
After studying the potential of the ETSPU to group fruit juices, the abil-
ity of the system to quantify the syrup content of the samples was examined.
In PLS2 models the sensor readings were coupled to the information on the
syrup content found on the labels of the multifruit juices. The results are
shown in Table 3.19. The PLS2 model gives good results for almost all
nine syrups. The slopes and correlations of all calibration and validation
models are high and close to respectively one and 100%. Relatively large
errors, RMSE values, are found in the prediction of blend-9 fruit. This can
be explained by the complexity of this syrup. It is, as mentioned before,
composed out of nine different fruit. Some of the fruit were also analyzed
individually as syrups. The large errors in the prediction of apple have to
be seen relative to its concentration in the different multifruit juices. In
both Benefits Immunity and Vitality, the apple content is the highest of
84 3.3 Results
all individual fruit syrups. Despite the small amounts of lemon syrup and
passion fruit syrup, these fruit can be predicted perfectly in the multifruit
juices. This proves that the ETSPU is very accurate and can predict low
concentrations in a complex matrix.
Table 3.19: PLS2 models based on the ETSPU readings of the multifruit juices
(logarithm of percentage). Cross-validation was used to validate the model. The
offsets, RMSEC and RMSECV values are given in % v/v.
Syrup Slope Offset Correlation RMSEC
RMSECV
Blend-9 fruit Calibration 0.98 0.23 0.99 1.62
Validation 0.94 0.53 0.98 2.70
Lemon Calibration 0.99 0.004 0.99 0.05
Validation 0.95 0.007 0.98 0.09
Orange Calibration 0.99 0.007 0.99 0.39
Validation 0.99 0.03 0.99 0.62
Passion fruit Calibration 0.99 0.0006 0.99 0.03
Validation 0.99 0.002 0.99 0.05
Apple Calibration 0.98 0.58 0.99 3.38
Validation 0.94 2.86 0.98 5.64
Red grape Calibration 0.99 0.07 0.99 0.88
Validation 0.96 0.42 0.98 1.44
Elderberry Calibration 0.99 0.04 0.99 0.52
Validation 0.96 0.25 0.98 0.86
Cherry Calibration 0.99 0.01 0.99 0.14
Validation 0.96 0.07 0.98 0.23
Strawberry Calibration 0.99 0.02 0.99 0.19
Validation 0.96 0.09 0.98 0.32
Electronic tongue technology 85
3.4 Discussion
3.4.1 Classification of apple and tomato cultivars and quan-
tification of taste compounds
In a first experiment, dealing with two fruit species, the ability of the ET-
SPU to classify fruit based on their chemical composition and to quantify
their most important taste compounds was studied. Unstable sensors were
discarded from the data matrix before analysis, based on their CV value.
Since the same four sensors were considered unstable during both apple and
tomato analysis, it can be concluded that these sensors are not suitable for
the analysis of fruit samples. However, the biological variability which was
present in this experiment could have influenced the CV values.
The ETSPU and HPLC can separate fruit of different species. Apple and
tomato cultivars are separated clearly using the multisensor system, based on
their most abundant sugars and acids. Within one species, the used ETSPU
system is able to classify cultivars only when they are very different from
each other. Only Pinova and Aranca are clearly separated from the other
apple or tomato cultivars. The separations mainly occur based on differences
in the glucose, fructose and sucrose content of the samples. Sensors 1, 12, 19
and 21 are related to the sugars present in both apple and tomato according
to the different correlation loadings plots. This, however, is unexpected
since the ETSPU consists of potentiometric sensors. Potentiometric sensors
are not directly sensitive to sugars in solution. Some correlations between
the sugars and minerals in the samples might explain this result. The good
classification performance of the ETSPU complements the results on other
food products which are reported in literature by Di Natale et al. (2000);
Vlasov et al. (2002); Legin et al. (2005b)
Using an artificial juice, the ETSPU was evaluated as a tool for quan-
tification of individual compounds in complex solutions. The results of the
PLS1 regressions showed that the ETSPU is able to quantify citric acid and
malic acid correctly in mixtures of chemical compounds. The prediction
models of glucose are less good, which is in line with the fact that the ET
86 3.4 Discussion
is an array of potentiometric sensors. Prediction of individual compounds
in fruit samples seemed to be more difficult than in artificial juices. The
PLS2 results, which predict two acids and three sugars in apple and tomato
juices, indicate that no good predictions can be made. Two remarks must
be given at this point:
� The set of sensors used in this experiment was not optimized. As dis-
cussed by Legin et al. (1997), the choice of an optimized sensor array
is crucial for analysis. First, the chemical sensors have to be prepared
using the most well-known and promising classes of solution sensing
materials. This allows to find a high sensitivity to certain species and
significant chemical durability and signal stability of the sensors. Sec-
ond, the correct set of sensors needs to be selected. Based on profound
knowledge of dependencies of the sensing performance, a wide range
of original non-specific sensing materials with a high cross-sensitivity
might be obtained for different multicomponent liquids. The selection
of a set of sensors which is sensitive towards several compounds of in-
terest, could be done using response surfaces. In the next experiment,
an optimized sensor array was used to analyze tomato samples.
� The reference and ETSPU measurements were not performed on the
same final samples. The reference measurements performed on the ap-
ple and tomato samples were carried out using HPLC, which requires
extraction. The ETSPU measurements, on the other hand, were per-
formed on juices, without further sample pretreatment. In the fol-
lowing experiment discussing the comparison between two ET’s, the
same sample preparation was performed for both the reference mea-
surements and the ET analysis. Both the reference measurements and
the ET analyses were performed on juices. More reliable results re-
garding the quantification of chemical compounds are listed in the next
part.
Electronic tongue technology 87
3.4.2 Comparison between two electronic tongues
In the first experiment, the potential of the ETSPU was evaluated in an
experiment dealing with apple and tomato. The ETSPU proved to be able
to detect large differences in the composition of the samples. In this second
experiment, the potential of an optimized ETSPU was compared to that of
a commercially available system. The ASTREE ET has been successfully
used and is, according to the manufacturer, suitable for qualitative and
quantitative research purposes (Tan et al., 2001; AlphaM.O.S., 2006). An
overview of the most important differences between both ET systems is
shown in Table 3.20.
The multidimensional signals of both ET’s required deletion of unstable
sensors from the data matrix before statistical analysis could be performed.
One of the sensors deleted from the ETSPU is sensor 18, which is the pH
sensor. The instability of this sensor can be found in the material it is made
off. Since the sensor contains oxide glass it often shows some instability in
samples containing organic material, like for instance food samples (Legin,
2007). The sensors of the ASTREE ET developed by Alpha M.O.S. were
very sensitive to drift. Most probably this instability is due to the cleaning
method prescribed by the Alpha M.O.S. company (AlphaM.O.S., 2001a,b).
Instead of a thorough cleaning with different rinsing and washing steps like
the ETSPU, Alpha M.O.S. prescribes only a short rinsing of the sensors in
distilled water. Drift in sensor signals is often a severe problem in sensor
technology. Holmin et al. (2001) proposed some techniques to correct linear
drift in ET’s based on component correction and additive correction. The
Alpha M.O.S. company does not acknowledge the problem of drift and, thus,
prescribes the data to be analyzed as they are (AlphaM.O.S., 2006).
Large differences were obtained in the potential of both ET’s to classify
six tomato cultivars. The tomato cultivars in this experiment were chosen
so that a wide range of taste compounds was present between the samples.
This, together with the fact that an optimized set of sensors was used in the
ETSPU, made it possible for the system to classify the cultivars based on
their chemical composition. However, it is not possible to classify all samples
88 3.4 Discussion
within the correct cultivar. The correlation loading show that the separation
is, again, mainly based on the differences in glucose and fructose content
between the samples. Sensors 8, 9, 10 and 11 have a large response when
exposed to juice of the cultivar Amoroso, which is a cultivar containing high
concentrations of the two sugars and two minerals which were studied. This
implies that these four sensor readings are correlated with these compounds.
Finding a one-to-one relation between chemical compounds and sensors is
not possible. Moreover, this would contradict the cross-sensitivity aspect of
this type of sensor arrays (Legin et al., 1999a).
Compared to the ETSPU and reference techniques, the ASTREE ET
can classify the tomato samples correctly within each of the six cultivars.
However, despite the manufacturer’s promises, there is no proof that this
classification is based on the studied taste compounds. The separation of
two cultivars from the other four, could not be related to the sugar or acid
content of the samples. These classification results are in contrast to the
results of Bleibaum et al. (2002). This paper discusses the results of the
ASTREE ET analysis of a series of apple juices. Using the ASTREE ET,
apple juices can be classified based on their taste according to the author.
Since apples, in general, contain the same taste compounds as tomatoes
(Table 3.5), the results of the experiment of Bleibaum et al. (2002) should
be comparable to the experiment described in this thesis. In the experiment
of Bleibaum et al. (2002), however, a different set of sensors was used, i.e.
the commercially available set #2 (sensors ZZ, BB, CA, BA, AB, HA and
CB). The commercially available set #1 (sensors ZZ, BA, BB, CA, GA, HA
and JB) was chosen, based on their selectivity to sugars, acids and minerals
(AlphaM.O.S., 2001a,b, 2006), for the analysis of the tomato samples in this
experiment. The difference of two sensors in the arrays could explain the
incompatibility between the results found in literature and those described
in this thesis.
The ability of both multisensor systems to quantify the chemical content
of the samples was examined using PLS2 models in which the sensor read-
ings were coupled to the results of the reference measurements performed
on samples, which had undergone the same sample preparation. The ET-
Electronic tongue technology 89
SPU shows PLS2 models that can predict chemical compounds present in a
tomato matrix. Since the ETSPU contains potentiometric sensors, the good
prediction models of glucose and fructose are caused by correlations between
these compounds and the minerals present in the samples. Tomatoes with a
high sugar content in this experiment also have high mineral concentrations.
Validation of the prediction models on a completely independent dataset is
required in the future. The results of the ETSPU, both for classification
of cultivars and quantification of compounds, are better than the results of
the first experiment in which apple and tomato samples were analyzed. The
explanation for this is threefold:
� The sensors were optimized between the two experiments so that they
were better suited for the analysis of fruit samples.
� The analysis of the reference technique and the ET analysis are per-
formed on exactly the same samples.
� The tomato cultivars chosen for this experiment comprised a large
range, if not the largest range possible, of sugars and acids in tomato.
With this experiment, the ETSPU has proved its potential to classify cul-
tivars based on their chemical composition and quantify their taste com-
pounds. A large range of taste compounds was used. Since most tomato
and other fruit cultivars do not differ that much in chemical composition, it
would be advisable to perform an extra experiment, using this set of sensors,
to determine the sensitivity of the system.
The results of the PLS performed on the data from the ASTREE ET
are, as for the classification, completely different from those of the ETSPU.
The correlations between the measured and predicted concentrations are low
and all RPD values are below two, indicating that the models are not good.
From these results can be concluded that the ASTREE ET equipped with
this set of sensors is not able to predict individual chemical compounds in
tomato juices, despite the fact that Alpha M.O.S. advices this sensor array
for analysis of sugars, acids and minerals (AlphaM.O.S., 2001a,b, 2006).
90 3.4 Discussion
Table
3.2
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rman
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om
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Electronic tongue technology 91
3.4.3 Quality control of fruit juices
In previous experiments, the potential of the ETSPU and a commercially
available ET was studied. The third experiment evaluated the ETSPU for
use in quality control of fruit juices.
In a first step, unstable sensors were again identified based on the CV
values. None of the sensors of the ETSPU were sensitive to drift during the
experiment.
Second, a PCA was carried out to analyze the multidimensional data
structure measured with the ETSPU. On the score plot samples with abnor-
mal compositions were indentified. Since blend-9 fruit contains pineapple,
orange, passion fruit, mandarin, grapefruit, banana, mango, guava and pa-
paya, both orange and passion fruit are positioned close to blend-9 fruit in
the score plot of the PCA. Also, mixtures of syrups with a similar composi-
tion are located closer together than mixtures with different contents. This
systematic trend in taste evolution illustrates that the ETSPU has potential
for applications in food quality control and food adulteration. The sensitiv-
ity of the system was also illustrated in a second analysis where it was tried
to reassemble the original juices based on the individual syrups according
to the juice components indicated on the labels. It seemed impossible to
group the three multifruit juices together with the mixtures which have the
same fruit composition, because of the presence of other compounds, like the
vitamins, aloe vera puree and minerals in the multifruit juices (Table 3.3).
The ETSPU, as seen in the previous experiment, is very sensitive to miner-
als. Small differences in mineral content can already cause large differences
in the sensor readings. Information and ETSPU measurements of these ex-
tra compounds present in the multifruit juices are necessary to make good
models to determine abnormal fruit composition in commercially available
juices. Rudnitskaya et al. (2001) found that a difference of only 1% in the
water content of a fruit juice is detectable with the ETSPU. Detailed in-
formation on the extra compounds was not available, due to the company’s
policy on product secrecy.
92 3.4 Discussion
To validate the system for the determination of abnormalities in the pro-
duction of fruit juices, an extra experiment should be performed evaluating
the sensitivity of the sensors to small additions of a compound in a multifruit
juice. Legin et al. (2005b) studied the potential of the ETSPU in combi-
nation with PCA to determine the quality of vodka. The authors analyzed
samples from different producers both complying and not complying with
quality standards. Some samples contained higher than allowed quantities
of aldehydes, ethers, oils and/or n-propanol or were prepared with water
of bad quality. The ETSPU was found capable to distinguish vodka of dif-
ferent quality standards. The possibility to detect a lower or higher than
normal content of a syrup in the multifruit juices, has not been tested in
the experiment described in this thesis. By adding syrups to the multifruit
juices and measuring the resulting samples with the ETSPU, the sensitivity
of the system to detect small differences using PCA could be evaluated. Also
PLS regression techniques and multivariate statistical process control might
contribute to detect deviation from the regular multifruit juice composition.
Finally, PLS2 models were used to predict the exact concentrations of
fruit syrup in the multifruit juices. Such models could be used in quality
control of juice blending unit operations. From the results it is clear that,
using the ETSPU, it is possible to predict the fruit content of the multifruit
juices. The prediction of the blend-9 fruit and apple syrup is not as good as
the other syrups. This might be explained by the complexity of this syrup.
It is, as mentioned before, composed of nine different fruit. Despite the small
concentrations of lemon and passion fruit, these fruit are predicted perfectly
in the multifruit juices. This proves that the ETSPU is very sensitive and
can predict low concentrations in a complex matrix. Small differences in the
fruit content of a multifruit juice can be measured, which was the objective
of the experiment.
This preliminary experiment indicates that the ETSPU in combination
with multivariate statistical techniques is a powerful tool for the detection of
products of unacceptable or deviating quality, which has important practical
implications towards the food industry.
Electronic tongue technology 93
3.5 Conclusions
The potential of ET technology to classify food samples according to simi-
larity in their chemical content and to quantify their taste compounds was
studied in three experiments.
In a first experiment the ETSPU proved to be a good tool to classify
both apple and tomato cultivars based on large differences in their chemical
content. However, the ETSPU was not sensitive enough to accurately de-
tect individual sugars, and hence, discrimination between cultivars is mainly
based on the organic acid content and matrix effects. Artificial juices were
used to study the predictive ability of the multisensor system. Good results
were obtained for the prediction of both malic acid and citric acid. Predic-
tion of the sugar and acid content in the real apple and tomato samples using
this ET, however, is not possible using this set of sensors. This could be due
to the small concentration ranges or the difference in sample preparation
between the reference analysis and ETSPU measurements.
The potential of an optimized ETSPU was compared to a commercially
available system, the ASTREE ET developed by Alpha M.O.S., in a sec-
ond experiment. Both multisensor systems show considerable differences in
measurement protocol. The ETSPU demands little sample preparation and
only a relatively small amount of sample is needed. Cleaning of the sensors
takes more time, but because of this the sensors show almost no drift in
the time frame of the performed experiment. The commercially available
ASTREE ET requires a large amount of centrifuged sample. The sensor
cleaning protocol is rather limited and might result in sensor drift. Both
ET’s are able to classify tomato cultivars to some extend based on their
sugar, acid and mineral content. The ETSPU can quantify individual taste
compounds in a tomato matrix, while the ASTREE ET cannot quantify the
concentration of any of the studied compounds.
In a final experiment the potential of the ETSPU as a tool for quality
control was studied. Using this system it is possible to group multifruit juices
and fruit syrups. Information on the extra compounds present in the multi-
94 3.5 Conclusions
fruit juices is necessary to make a good PCA model to determine deviations
from the recipe of the multifruit juices. The ETSPU, however, can quantify
the fruit syrup content in the multifruit juices. Even low concentrations are
predicted very accurately in this complex matrix.
In this chapter, ET technology proved to be a good tool for identification,
classification, determination and quality control of fruit juices with different
chemical compositions. This indicates the possibilities of the system as an
instrument in the detection of fruit from different geographical origins or
orchards. The ET could be introduced in experiments dealing with the con-
servation of the quality of products and brands by the detection of artifacts
or spoilage. Using the ET it may also be possible to identify products with
different storage conditions or durations.
Chapter 4
Fourier transform infrared
spectroscopy
4.1 Introduction
Human evaluation has been the primary method of quality assessment, but
has many limitations. One of the large limitations of the human eye is
its use of only a very narrow band of the vast electromagnetic spectrum.
Some quality attributes, external and internal defects and compositional
factors are more readily detectable in the region outside the visible range,
e.g. ultraviolet (UV) and infrared (IR).
IR spectroscopy has been used extensively as an analytical technique to
gather information about both the structure and purity of a compound. The
IR region is divided into three regions: the NIR (8000 cm−1-4000 cm−1),
mid-IR (4000 cm−1-400 cm−1) and far-IR (400 cm−1-50 cm−1). Despite the
fact that mid-IR region is the region of greatest practical use to the organic
chemist, it has been less employed than the NIR region in food analysis.
Most foods contain large amounts of water that strongly absorbs mid-IR
radiation. The poor transmission and often high scattering of many sam-
ples means that usually very little light can be detected. The development
of Fourier transform infrared spectroscopy (FTIR) has renewed interest in
95
96 4.1 Introduction
the potential of mid-IR for food analysis. FTIR spectrometers using an in-
terferometer provide more energy to the sample, scan a lot faster and have
the capability to co-add data so that within a short time spectra can be
produced from poorly transmitting samples with acceptable signal-to-noise
ratios. Mid-IR has significant advantages over NIR for spectral assignment,
resolution and ease of quantification. Another advantage is that mid-infrared
spectra provide information about the physical and chemical states of indi-
vidual compounds (Wilson and Goodfellow, 1994; Griffiths and de Haseth,
2007). The development of FTIR has led to an increased interest in sample
presentation techniques. Today there is a wide choice of sample accessories
available with different designs and approaches (Gunasekaran, 2001; Grif-
fiths and de Haseth, 2007). Internal reflection, also known as attenuated
total reflectance (ATR), is one of the most powerful FTIR methods because
of its flexible sample presentation. ATR offers interesting possibilities for the
analysis of both solid and liquid samples. The determination of the authen-
ticity of fruit juices and differentiation of commercial juices based on sugars
and phenolic compounds was studied by He et al. (2007). ATR-FTIR suc-
cessfully resulted in the quantification of organic acids and sugars in apple
juices (Irudayaraj and Tewari, 2003) and caffeine in soft drinks (Paradkar
and Irudayaraj, 2002). In the last few years there has been a shift towards
flow injection FTIR analysis for the quantification of chemical compounds.
This will be discussed in detail in Chapter 5.
As literature shows, a lot of research has been performed to study the
potential of ATR-FTIR for the classification of samples and the determi-
nation of different compounds. Until now, however, no report has been
made regarding the use of ATR-FTIR to measure taste attributes of fruits.
A structured study, dealing first with the measurement of taste compounds
and second with taste as percieved by a sensory panel, will be a contribution
to ATR-FTIR research.
The objective of this chapter is to develop a high throughput technique
based on ATR-FTIR to classify fruit samples according to their most im-
portant taste compounds and to quantify these taste compounds. Hereto,
the potential of ATR-FTIR as a classification and quantification instrument
Fourier transform infrared spectroscopy 97
was studied using a wide variety of samples.
� In a first experiment the potential of ATR-FTIR to analyze taste com-
pounds will be studied. Different solutions of pure compounds and
mixtures will be measured with ATR-FTIR to determine the impor-
tant absorptions of IR light which are characteristic for each taste
compound. Calibration models will be built based on the absorbance
spectra of the pure compounds to predict their concentrations.
� Using both apple and tomato samples, the potential of ATR-FTIR
to classify apples and tomatoes according to their taste components
and to quantify their sugars and acids will be investigated in a sec-
ond experiment. The results will be compared to those of a reference
technique and the ETSPU.
� The potential of ATR-FTIR to classify three tomato cultivars and
quantify their taste compounds will be evaluated as a function of sam-
ple preparation technique. The samples are analyzed both as extracts
and juices. The results of these measurements will be compared to
those of an enzyme based reference technique.
� In a fourth experiment, dilutions of tomato juice and standard addi-
tions to the same samples will be analyzed with ATR-FTIR to build
calibration models based on a larger range of concentrations. With this
experiment, the ability of the system to quantify taste compounds will
be investigated thoroughly.
� Finally, in a last experiment, the potential of ATR-FTIR as a tool for
quality control will be studied since errors in the production process of
all food products should be detected as soon as possible. Different mul-
tifruit juices and the individual syrups they are made of are analyzed
using ATR-FTIR. The ability of this technique to detect differences
in the fruit composition of the multifruit juices will be studied. The
results are compared to those of the ETSPU.
This chapter is divided in four main sections. In Section 4.2 the materials
and methods are described. The section discusses the samples used in the
98 4.2 Materials and Methods
different experiments and settings of the ATR-FTIR system in detail. Re-
sults of experiments on apples, tomatoes and multifruit juices are reported
in Section 4.3. In Section 4.4 the results are discussed and compared to refer-
ence measurements, ET results and literature findings. Concluding remarks
are formulated in Section 4.5. The results of the experiments described in
this chapter are published by Beullens et al. (2004, 2005a,b, 2006a) and
Rudnitskaya et al. (2006).
4.2 Materials and Methods
4.2.1 Samples
4.2.1.1 Taste compounds
Different concentrations of chemical components were analyzed using ATR-
FTIR in a experiment 1. The three sugars and two acids of interest are
glucose, fructose, sucrose, citric acid and malic acid. A sample with a high
content of one of the studied compounds was made in quadruple, to intro-
duce repetitions in the experiment, and then diluted with distilled water.
An extended dilution series was made for each compound with following
concentrations: 250 g/L, 200 g/L, 150 g/L, 100 g/L, 75 g/L, 50 g/L, 25
g/L, 20 g/L, 15 g/L, 10 g/L, 8 g/L, 6 g/L, 4 g/L, 2 g/L, 1 g/L, 0.75 g/L,
0.50 g/L, 0.25 g/L, 0.20 g/L, 0.15 g/L, 0.10 g/L, 0.075 g/L and 0.05 g/L.
Extremely high concentrations were analyzed next to low concentrations,
which are more realistic in fruit samples, to evaluate the potential of ATR-
FTIR to measure differences in concentrations. All samples were analyzed
immediately after preparation. The chemical compounds were purchased at
Sigma Aldrich (Steinheim, Germany).
Mixtures of three sugars and three acids, glucose, fructose, sucrose, citric
acid, malic acid and glutamate, were also prepared. Glutamate was added
in this experiment, since literature showed that this is an important taste
compound of tomato (Petro-Truza, 1987). To determine the concentrations
Fourier transform infrared spectroscopy 99
Table 4.1: Levels of fructose, glucose, sucrose, citric acid, malic acid and glutamate
used in the BB design (g/L).
Compound
Glucose 25 12.5 0
Fructose 25 12.5 0
Sucrose 2 1 0
Glutamate 3 1.5 0
Citric acid 8 4 0
Malic acid 2 1 0
of the compounds in the mixtures, a Box-Behnken (BB) design with six fac-
tors and three levels per factor was introduced (NIST/SEMATECH, 2007).
The BB calculations were performed in SAS version 9.1 (SAS Institute Inc.,
Cary, USA). Using this design, 54 samples were analyzed. The samples com-
prised six repetitions of the centerpoint. To this design, 16 extra samples
were added, to give a total of 70 samples to be analyzed. The extra samples
were mixtures which were not part of the original Box-Behnken design with
concentrations in between the ones determined in the original design. The
samples were added to include concentrations of acids and sugars which are
common in tomato. An overview of the levels of sugars and acids used in
the BB design is shown in Table 4.1. All mixtures were made at the same
moment and stored in falcon tubes at −80 ◦C until analysis. The samples
were analyzed in a random order. Each sample was analyzed 5 times using
ATR-FTIR.
4.2.1.2 Classification of apple and tomato cultivars and quantifi-
cation of taste compounds
Apples (Malus× domestica Borkh.) of five cultivars were used in experiment
2: Cox, Elstar, Golden Delicious, Jonagold and Pinova. The apples were
purchased at the local supermarket. Twenty apples per cultivar were cut into
pieces and frozen in falcon tubes using liquid nitrogen. The frozen samples
were stored at −80 ◦C until further sample preparation was performed.
100 4.2 Materials and Methods
Four tomato cultivars (Lycopersicon esculentum Mill.) were selected for
experiment 2: Aranca, Climaks, Clotilde and DRW 73-29. Twenty toma-
toes per cultivar were harvested at the Proefstation voor de Groenteteelt in
Sint-Katelijne-Waver (Belgium) at ripeness stage 5 (light red class) (USDA,
1975). The tomatoes were stored for one day at ambient atmosphere (18 ◦C
and 80% relative humidity). The day after harvesting the tomatoes were
cut into pieces, put into falcon tubes and frozen using liquid nitrogen. The
frozen samples were stored at −80 ◦C until further sample preparation was
performed.
The apple and tomato samples were analyzed using ATR-FTIR, the
ETSPU (Chapter 3) and HPLC.
4.2.1.3 Extracted samples versus juices
Three tomato cultivars (Lycopersicon esculentum Mill.), Clotilde, Bonaparte
and Tricia, were analyzed in experiment 3. Ten tomatoes per cultivar were
obtained at the fruit- and vegetable Auction of Mechelen (Belgium) and the
Auction of Hoogstraten (Belgium). All fruit were picked at ripeness stage 5
(light red class) (USDA, 1975). Five tomatoes per cultivar were stored for
one day at ambient atmosphere, 18 ◦C and 80% relative humidity. The other
five fruit of each cultivar were stored for one week at the same conditions.
After storage for one day or one week, the tomatoes were cut into pieces
and frozen in falcon tubes using liquid nitrogen. The frozen samples were
stored at −80 ◦C until further sample preparation was performed.
Two different sample preparations were carried out on each sample.
Hereto, every sample was split in two. From one part an extract was pre-
pared (Chapter 3) and analyzed with an enzyme based reference technique
(EHT) and ATR-FTIR. The other part was centrifuged at 20000 g during 5
minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville, USA) and
analyzed with EHT and ATR-FTIR. No HPLC analysis was performed on
the juices, since no reliable analysis is possible without sample extraction.
Fourier transform infrared spectroscopy 101
4.2.1.4 Dilutions and standard additions
Six tomato cultivars were chosen for an experiment (experiment 4) dealing
with dilution and standard addition to the samples: Macarena, Growdena,
Tricia, Admiro, Loredana and a cherry tomato. Ten tomatoes of the first
five cultivars were harvested at the Proefstation voor de Groenteteelt in
Sint-Katelijne-Waver (Belgium) at ripeness stage 5 (light red class) (USDA,
1975). The cherry tomatoes were purchased at the local supermarket. The
tomatoes were stored for one day at ambient atmosphere (18 ◦C and 80%
relative humidity). The day after purchase, the fruit were juiced and frozen
in falcon tubes using liquid nitrogen. The frozen samples were stored at
−80 ◦C until further sample preparation was performed.
Table 4.2: Composition of the mixtures used for standard addition (g/L).
Compound Mixture 1 Mixture2
Glucose 13.5 27
Fructose 14 28
Sucrose 5.5 11
Glutamate 5 10
Citric acid 11.5 23
Quinic acid 0.3 0.5
Malic acid 5.5 11
Tartaric acid 1.5 3
Just before measurement the samples were defrosted and divided into
four parts of 10 mL. One part of the juice was kept separately and analyzed
as such. A second part of the juice was diluted with distilled water to 1/2
of its original concentration. Finally, to the other two parts, a mixture of
sugars and acids was added. A stock solution of a mixture of pure chemical
components was prepared as shown in Table 4.2. The mixture and a 1/2
dilution of the mixture were added to the tomato samples. The chemical
compounds used for the standard addition were purchased at Sigma Aldrich
(Steinheim, Germany). After addition of the mixtures or dilution of the
juices, the samples were mixed thoroughly. All samples were analyzed using
102 4.2 Materials and Methods
ATR-FTIR. The diluted and the original samples were also analyzed using
the EHT reference method.
4.2.1.5 Quality control of fruit juices
Three multifruit juices of the brand Sunland (Sunnyland, Turnhout, Bel-
gium) were purchased at the local supermarket: Fruitdrink ACE, Benefits
Vitality and Benefits Immunity (experiment 5). The composition of the
multifruit juices, as mentioned on the label, is given in Chapter 3 (Table
3.3). The individual syrups and juices used to compose the multifruit juices
were provided by Sunnyland Distribution (Turnhout, Belgium): blend-9
fruit (fixed blend made out of pineapple, orange, passion fruit, mandarin,
grapefruit, banana, mango, guava and papaya), lemon, orange, passion fruit,
apple, red grape, elderberry, cherry and strawberry. The juices and syrups
were frozen in falcon tubes using liquid nitrogen and were stored at −80 ◦C
until further sample treatment. Using the individual syrups 11 mixtures
were made of which three resembled the composition of the multifruit juices.
Mixtures 1, 2 and 3 respectively correspond to Fruitdrink ACE, Benefits Vi-
tality and Benefits Immunity. The composition of all 11 mixtures is listed
in Chapter 3 (Table 3.4). In addition to these mixtures, syrup was added
to two of the multifruit juices. Passion fruit and cherry syrup were added
to respectively Benefits Immunity and Benefits Vitality with a syrup:juice
ratio of 1:9, 2:8, 3:7, 4:6 and 5:5. The nine individual syrups, the three
multifruit juices and the 11 mixtures were analyzed in triplicate using the
ETSPU (Chapter 3) and ATR-FTIR. The juices with addition of a syrup
were only analyzed using ATR-FTIR.
4.2.2 ATR-FTIR
Three different FTIR instruments were used in this thesis. An overview
of the instruments and settings is given in Table 4.3. All analyses were
performed on juices and centrifuged samples. The apple and tomato sam-
ples of experiment 2 were analyzed on a Bio-Rad FTS 6000 spectrometer
Fourier transform infrared spectroscopy 103
(Bio-Rad, Hercules, USA) at the Department of Agricultural and Biological
Engineering of Penn State University (State College, USA). The samples
were defrosted in a warm water bath at 25 ◦C and centrifuged at 20000 g
during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville,
USA). One mL of the supernatants was put on a ZnSe crystal with 9 re-
flections for measurement. The individual chemical compounds (experiment
1) and the samples of experiments 3 and 4 were analyzed on a Bruker IFS
66v/S spectrometer (Bruker, Karlsruhe, Germany) at the Center for Sur-
face Chemistry and Catalysis of the K.U. Leuven (Leuven, Belgium). The
tomato juices were defrosted before analysis and centrifuged at 20000 g
during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo, Bensenville,
USA). The measured extracts were prepared similarly as for the HPLC mea-
surements (Chapter 3). One mL of supernatants of the samples was put on
an AMTIR crystal with 9 reflections. The mixtures of experiment 1 and the
samples of experiment 5 were analyzed on a Bruker Tensor 27 spectrometer
(Bruker, Karlsruhe, Germany). All measurements were performed on cen-
trifuged juices. The juices were defrosted before analysis and centrifuged
at 20000 g during 5 minutes (Hawk 15/05 Refrigerated centrifuge, Sanyo,
Bensenville, USA). One mL of supernatants of the samples was put on the
AMTIR crystal. Temperature control was not included in the experimental
set-up, since the effects of long-term response instability can be eliminated
by using background spectra recorded immediately before or after the sam-
ple spectra in case of a short measurement time (MacBride et al., 1997).
Measurement time per sample is about 30 seconds for all FTIR instruments
at the settings used in this chapter. Between all measurements the ZnSe or
AMTIR crystal was carefully cleaned using distilled water and dried with a
special lens cleaning tissue (Schleichner and Schuell, Whatman International
Ltd., Maidstone, UK).
104 4.2 Materials and Methods
Table
4.3
:F
TIR
inst
rum
ents
an
dse
ttin
gs
use
dfo
rth
ean
aly
sis
of
diff
eren
tty
pes
ofsa
mp
les.
Equ
ipm
ent
Det
ecto
rA
TR
cell
Co-
adde
dR
esol
utio
nB
ackg
roun
dR
ange
Soft
war
e
scan
s
FT
S60
00B
io-R
adD
TG
SZ
nSe
644
cm−
1B
efor
eev
ery
4sa
mpl
es18
00-8
00cm
−1
Win
-IR
Pro
TM
2.5
IFS
66v/
SB
ruke
rM
CT
AM
TIR
128
4cm
−1
Bef
ore
ever
ysa
mpl
e18
00-9
00cm
−1
OP
US
5.5
Ten
sor
27B
ruke
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128
4cm
−1
Bef
ore
ever
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US
5.5
Fourier transform infrared spectroscopy 105
4.2.3 Reference techniques
4.2.3.1 HPLC
The apple and tomato samples were analyzed using HPLC. The sample
preparation and measurement of the sugar and acid content of the apple
and tomato samples was explained in detail in Chapter 3.
4.2.3.2 Enzymatic high throughput technique
An enzymatic high throughput method, EHT, was used as a reference tech-
nique to evaluate the sugar and acid content of the tomato extracts and
juices. Details on the sample preparation and operational settings are given
in Chapter 3.
4.2.4 Statistical analysis
The ATR-FTIR data were preprocessed before the multivariate analysis.
The first derivative of the absorption spectra was calculated using the Savitsky-
Golay algorithm (second order polynomial, 5 points at each side). The first
derivative absorption spectra were used for further data analysis.
Multivariate data analysis was applied for both qualitative and quanti-
tative analysis using ATR-FTIR. Partial least squares-discriminant analysis
(PLS-DA) was used for data visualization and clustering of observations in
the data structure. Using this technique, intra-cultivar effects are minimized
and inter-cultivar effects are maximized. The analysis was performed on the
covariance matrix. Outliers were deleted from the analysis based on their
scores, leverages (distance to the model centre for each object summarized
over all components) and residuals (Geladi and Dabakk, 1995). The results
of the PLS-DA performed on the ATR-FTIR data were compared to those
of the reference techniques and ETSPU.
Principal component analysis (PCA), an unsupervised method, was used
106 4.2 Materials and Methods
as a data exploration technique on the data of the fruit juices, syrups and
mixtures. The potential of ATR-FTIR as a tool for quality control was
examined using this technique. The multifruit juices, syrups and their mix-
tures were grouped using PCA (Johnson and Wichern, 1992).
Partial least squares analysis (PLS) was performed to study the pre-
dictive capacity of ATR-FTIR for individual compounds and syrups. The
calibration models were validated using cross-validation, where a small set
of randomly selected samples is left out of to construct the model and is
used afterwards for validation purposes. The limit of detection (LOD) was
calculated from the 95% confidence interval for each compound, both in a
watery solution and in the tomato matrix. The 95% confidence interval is
expressed as ± 1.96RMSEC. PLS2 was used for the prediction of the sug-
ars and acids in apples, tomatoes and the prediction of syrups in multifruit
juices. In a PLS2 analysis, all compounds of interest (Y variables) are re-
lated together to the absorbances (X variables). The results of the HPLC
and/or EHT measurements were taken as references for the assessment of
individual chemical compounds in apple and tomato. For the prediction of
individual compounds in experiment 1, PLS1 was used. In this case, the
concentration of the sugars and acids are related one by one (one Y vari-
able) to the absorbances (X variables) (Martens and Naes, 1998). Prediction
models having a correlation above 0.90, high slope and low offset were con-
sidered to be good. The correlation gives information about the quality of
the model, but gives no direct information about the prediction accuracy.
The RPD value is a factor which indicates the accuracy. An RPD value be-
tween 2 and 2.5 makes approximate quantitative predictions possible. For
values between 2.5 and 3, and above 3, the prediction is classified as good
and excellent, respectively (Saeys et al., 2005).
For data analysis two different computer software programs were used:
The Unscrambler version 9.1.2 (CAMO Technologies Inc., Olso, Norway)
and SAS version 9.1 (SAS Institute Inc., Cary, USA).
Fourier transform infrared spectroscopy 107
4.3 Results
4.3.1 Taste compounds
The spectra of the three sugars and two acids in a concentration of 50
g/L are shown in Figure 4.1. The spectra of the three sugars show a lot of
similarities. Glucose, fructose and sucrose do not absorb significant amounts
of IR light between 1800 cm−1 and 1475 cm−1. The highest absorption bands
were found between 1170 cm−1 and 900 cm−1. Differences in the absorbance
peaks of the three sugars are visible in this part of the spectrum. Glucose
shows separated peaks, with two clearly defined peaks at 1080 cm−1 and 1034
cm−1. The spectrum of fructose is a lot smoother in this area, compared to
glucose, with only small peaks and one large peak at 1063 cm−1.
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
1799
1753
1707
1660
1614
1568
1522
1475
1429
1383
1336
1290
1244
1198
1151
1105
1059
1012 966
920
Abs
orba
nce
Wavenumber (cm-1)
Glucose FructoseSucrose Citric acidMalic acid
Figure 4.1: ATR-FTIR absorbance spectra of glucose, fructose, sucrose, citric
acid and malic acid.
108 4.3 Results
The spectrum of sucrose has a different shape between 1170 cm−1 and
900 cm−1. There are three clear peaks and one zone with an overlap of two
peaks, which make the spectrum of sucrose unique. The characteristic peaks
for sucrose are at 1055 cm−1, 997 cm−1 and 924 cm−1.
The spectra of the two acids show very different absorptions compared
to the sugars. The spectra of citric acid and malic acid have a peak at
1722 cm−1. Citric acid furthermore shows a high absorbance around 1223
cm−1. The spectrum of malic acid shows three overlapping peaks between
1321 cm−1 and 1140 cm−1 and a defined peak at 1109 cm−1. The region
of the spectrum between 1500 cm−1 and 900 cm−1, is called the fingerprint
region. Peaks in this region are very difficult to assign to specific molecular
vibrations. However, this complexity has an important advantage in that it
can serve as a fingerprint for a given compound. Consequently, by referring
to known spectra, the fingerprint region can be used to identify a compound.
C C C C CH C H
H OH H
OH
OO
HO
HO O
OH
OO
HOC C C C
H H
H OH
C C H
HCH
CHC O
H
OH
OH
OHHO
CH2OHCitric acid Malic acid
H
H
HO
OH
HO C C
C
O
CH
CH2OH
HOH2C
Fructose Glucose
OHC C H
HCH
CHC O
HOH
HO
CH2OH
H
H
HO
OHC C
C
O
CH
CH2OH
HOH2C
OSucrose
Figure 4.2: Chemical structures of glucose, fructose, sucrose, citric acid and malic
acid.
Fourier transform infrared spectroscopy 109
In Table 4.5 and Table 4.4 an overview of the wavenumbers at which
absorbance peaks are present in the spectra of glucose, fructose, sucrose,
citric acid and malic acid. Also, the specific vibrations that absorb at these
wavenumbers are reported. Figure 4.2 shows the chemical structures of the
five compounds of interest.
Table 4.4: Wavenumbers and vibrations with high absorbances in two acids (Grif-
fiths and de Haseth, 2007).
Compound Wavenumber Vibration
Citric acid 1722 cm−1 C=O stretching
1400 cm−1 CH bending OH bending
1317 cm−1 C-O stretching OH bending
1223 cm−1 C-O stretching
1132 cm−1 C-O stretching
1078 cm−1 C-O stretching C-C stretching
1057 cm−1 C-C stretching
Malic acid 1722 cm−1 C=O stretching
1400 cm−1 CH bending OH bending
1348 cm−1 C-O stretching OH bending
1275 cm−1 CH2 rocking C-O stretching
1230 cm−1 C-O stretching
1190 cm−1 CH2 wagging C-O stretching
1109 cm−1 C-O stretching C-C stretching
1039 cm−1 C-C stretching
110 4.3 Results
Table 4.5: Wavenumbers and vibrations with high absorbances in three sugars
(Griffiths and de Haseth, 2007).
Compound Wavenumber Vibration
Glucose 1452 cm−1 CH2 scissoring
1422 cm−1 CH2 scissoring CH bending
1360 cm−1 C-O stretching OH bending
1315 cm−1 C-O stretching OH bending
1200 cm−1 C-O stretching
1153 cm−1 CH2 wagging C-O stretching
1105 cm−1 C-O stretching C-C stretching
1080 cm−1 C-O stretching C-C stretching
1034 cm−1 C-O stretching C-C stretching
991 cm−1 C-C stretching
912 cm−1 C-C stretching
Fructose 1452 cm−1 CH2 scissoring
1412 cm−1 CH2 scissoring CH bending
1342 cm−1 C-O stretching OH bending
1252 cm−1 CH2 rocking C-O stretching
1180 cm−1 CH2 wagging C-O stretching
1155 cm−1 CH2 wagging C-O stretching
1101 cm−1 C-O stretching C-C stretching
1082 cm−1 C-O stretching C-C stretching
1063 cm−1 C-O stretching C-C stretching
1014 cm−1 C-C stretching
978 cm−1 C-C stretching
966 cm−1 C-C stretching
918 cm−1 C-C stretching
Sucrose 1452 cm−1 CH2 scissoring
1422 cm−1 CH2 scissoring CH bending
1369 cm−1 C-O stretching OH bending
1329 cm−1 C-O stretching OH bending
1267 cm−1 CH2 rocking C-O stretching
1209 cm−1 C-O stretching
1134 cm−1 C-O stretching
1109 cm−1 C-O stretching C-C stretching
1055 cm−1 C-O stretching C-C stretching
997 cm−1 C-C stretching
924 cm−1 C-C stretching
Fourier transform infrared spectroscopy 111
The absorbance spectra of the dilution series between 250 g/L and 10
g/L of glucose is shown in Figure 4.3. The sample corresponding to the
spectrum with the highest absorption has the highest glucose concentration.
0.4
0.5
0.6
0.7
ce
250 g/L200 g/L150 g/L100 g/L75 g/L50 g/L25 g/L20 g/L
-0.1
0
0.1
0.2
0.3
1799
1753
1707
1660
1614
1568
1522
1475
1429
1383
1336
1290
1244
1198
1151
1105
1059
1012 96
6
920
Abso
rban
c
Wavenumber (cm-1)
20 g/L10 g/L
Figure 4.3: ATR-FTIR absorbance spectra of a dilution series between 250 g/L
and 10 g/L of glucose.
Using the ATR-FTIR first derivative spectra of the pure compounds,
PLS1 models were made to predict the concentration of a chemical com-
pound in a sample. PLS1 was preferred over PLS2 to make the prediction
models since only one compound was present in each of the measured sam-
ples. The dilution series between 50 g/L and 0.5 g/L was used to calibrate
the model since these concentrations are the most realistic to be found in
real fruit samples. PLS1 models were built using different variable selection
methods.
112 4.3 Results
Table 4.6: PLS1 validation models to predict individual compounds built on the
results of the ATR-FTIR measurements of pure compounds. Cross-validation was
used to validate the model. (Model 1: full spectrum; Model 2: two selected regions;
Model 3: 20 selected wavenumbers; Model 4: two selected wavenumbers)
Compound Model 1 Model 2 Model 3 Model 4
Malic acid R 0.99 0.99 0.99 0.99
RPD 27 37 33 9.2
Citric acid R 0.99 0.99 0.99 0.99
RPD 9.7 8.8 11 9.4
Sucrose R 0.99 0.99 0.99 0.99
RPD 39.8 35 35 7.0
Glucose R 0.99 0.99 0.99 0.99
RPD 18 25 28.5 5.8
Fructose R 0.99 0.99 0.99 0.99
RPD 14 21 22 5.1
The first model was built using the full first derivative spectrum. One PC
was selected to obtain the results shown in Table 4.6. High correlations and
RPD values are found for all compounds. The second model was built based
on the first derivative absorbances in two important regions of vibrations:
1800 cm−1 to 1650 cm−1 and 1140 cm−1 to 950 cm−1 resembling the main
absorbance regions of C=O vibrations of organic acids and C-O stretching
vibrations of carbohydrates respectively. One PC was used for all models.
The results of this model are equally good or better than those of the first
model. The RPD values of all prediction models are high, referring to a
good prediction performance. The good results of this model indicate that
no information necessary for the prediction of a compound was deleted from
the analysis. A third model was built using 20 wavenumbers per compound
which were selected as the minima and maxima of the first derivative spectra.
The selected wavenumbers are the inflection points of the main peaks of the
spectra. The results are better or equal to those of the previous models.
Fourier transform infrared spectroscopy 113
Table
4.7
:T
wo
sele
cted
wav
enu
mb
ers
per
chem
icalco
mp
ou
nd
use
dto
bu
ild
PL
S1
mod
els.
Com
poun
dSe
lect
edw
aven
umbe
rIn
flect
ion
poin
tV
ibra
tion
ofw
aven
umbe
r
Mal
icac
id17
66cm
−1
1722
cm−
1C
=O
stre
tchi
ng
1309
cm−
112
75cm
−1
C-O
stre
tchi
ngan
dC
H2
rock
ing
Cit
ric
acid
1367
cm−
114
00cm
−1
CH
and
OH
bend
ing
1243
cm−
112
23cm
−1
C-O
stre
tchi
ng
Sucr
ose
1155
cm−
111
34cm
−1
C-O
stre
tchi
ng
1062
cm−
110
55cm
−1
C-O
and
C-C
stre
tchi
ng
Glu
cose
1087
cm−
110
80cm
−1
C-O
and
C-C
stre
tchi
ng
1056
cm−
110
34cm
−1
C-O
and
C-C
stre
tchi
ng
Fruc
tose
1467
cm−
114
52cm
−1
CH
2sc
isso
ring
1074
cm−
110
63cm
−1
C-O
and
C-C
stre
tchi
ng
114 4.3 Results
A fourth model was made using only two wavenumbers per compound.
The two wavenumbers were selected from the maxima and minima of the
first derivative absorbance spectrum and the correlation loadings of these
wavenumbers. The selected wavenumbers are given in Table 4.7. One PC
was used in each PLS1 model. High correlations between the predicted and
known concentrations were found. The RMSECV values of all compounds
are higher than in the PLS1 analysis using the absorbance at all wavenum-
bers. The RPD values of all prediction models are lower than those of the
previous models, however, they are all still higher than 2. This indicates
that, using only two unique variables, a model can be made to predict a
compound using ATR-FTIR. Since absorbance spectra are influenced by
the presence of all compounds in a mixture, it is best to select more than
two variables per compound to make accurate predictions. From these four
models can be concluded that the selection of wavenumbers increases the pre-
dictive ability of ATR-FTIR. The best predictions, with the highest RPD’s
for all compounds, were found when 20 peaks were selected from the first
derivative spectra. Using this model, the LOD was calculated for each of
the compounds. Table 4.8 shows that the LOD’s are not very low, indicat-
ing that the system can only be used for the determination of compounds
present in high concentrations.
Table 4.8: Limit of detection (g/L) of taste compounds in pure solutions based
on calibration model 3.
Compound LOD
Fructose 1.6
Glucose 1.2
Sucrose 0.9
Citric acid 2.4
Malic acid 1.2
Fourier transform infrared spectroscopy 115
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
1799
1753
1707
1660
1614
1568
1522
1475
1429
1383
1336
1290
1244
1198
1151
1105
1059
1012 966
920
Abs
orba
nce
Wavenumber (cm-1)
Mixture SumGlucoseFructoseSucrose
Figure 4.4: ATR-FTIR absorbance spectra of a mixture of taste compounds and
the sum of their individual spectra.
To illustrate the additive aspect of FTIR, mixtures of sugars and acids
were analyzed. Figure 4.4 shows the absorbance spectra of one of the mix-
tures together with the sum of the spectra of the individual compounds.
PLS2 prediction models were built based on the minima and maxima
of the first derivative spectra of the mixtures. The results are not shown
because of their similarity with the previous results. High correlations, with
values of 0.99, were found for the prediction models for all compounds.
The RMSECV values are low, indicating low prediction errors, and the
RPD’s reach high values. Thus, ATR-FTIR is also able to predict individual
chemical compounds when they are present in mixtures.
116 4.3 Results
4.3.2 Classification of apple and tomato cultivars and quan-
tification of taste compounds
4.3.2.1 Classification with ATR-FTIR
The results of the reference measurement with HPLC performed on apple
and tomato samples were already presented in Chapter 3.
0.20
0.25
0.30
0.35
ce
Cox Elstar
Golden Jonagold
Pinova Aranca
Climaks Clotilde
DRW 73-29
-0.05
0.00
0.05
0.10
0.15
799
845
891
937
984
1030
1076
1123
1169
1215
1261
1308
1354
1400
1447
1493
1539
1585
1632
1678
1724
1771
Abso
rban
c
Wavenumber (cm-1)
Figure 4.5: Average ATR-FTIR absorbance spectra of five apple and four tomato
cultivars.
Figure 4.5 shows the average absorbance spectra for the five apple and
four tomato cultivars. The differences in the absorbance spectra are related
to differences in the chemical composition of the fruit (Table 3.5). A major
difference in the absorbance spectra of the apple cultivars is observed in the
wavenumber range between 1200 cm−1 and 900 cm−1. In this range both
Cox and Elstar show higher absorbance of IR light than the other three ap-
Fourier transform infrared spectroscopy 117
ple cultivars, which is related to C-O stretching vibrations that are present
mainly in sugars and to a lesser extend in acid. The results of the HPLC
measurements confirm that both cultivars do not have a high content of
those sugars. Cox, however, has a high content of sucrose, the main sugar
in apples, which absorbs more IR light than the other sugars around 930
cm−1. Pinova absorbs more around 1105 cm−1 and 1078 cm−1. The neg-
ative absorbance in the region between 1680 cm−1 and 1580 cm−1 results
from the background measurement of water. Water absorbs highly in this
area due to bending vibrations in water molecules which have shifted be-
cause of hydrogen bindings (Venyaminov and Prendergast, 1997; Garrigues
et al., 2000). Since the amount of water is different in the samples and the
background, a negative absorption is observed in the spectra. From the first
derivative absorbance spectrum (Figure 4.6), 20 wavenumbers were selected
for further data analysis. The selected wavenumbers are listed in Table 4.9.
0.004
0.008
0.012
e ab
sorb
ance
CoxElstarGoldenJonagoldPinova
-0.012
-0.008
-0.004
0.000
799
845
891
937
984
1030
1076
1123
1169
1215
1261
1308
1354
1400
1447
1493
1539
1585
1632
1678
1724
1771
Firs
t der
ivat
ive
Wavenumber (cm-1)
Figure 4.6: Average first derivative absorbance spectra of five apple cultivars.
118 4.3 Results
Table 4.9: Selected wavenumbers for apple and tomato based on the first derivative
absorbance spectra (Exp2: apple and tomato samples; Exp3: extracted and juiced
tomato samples; Exp4: tomato samples with standard additions).
Exp2 Exp2 Exp3 Exp4
Apple Tomato
1467 cm−1 1309 cm−1 1743 cm−1 1745 cm−1
1448 cm−1 1269 cm−1 1465 cm−1 1700 cm−1
1400 cm−1 1222 cm−1 1448 cm−1 1608 cm−1
1271 cm−1 1165 cm−1 1425 cm−1 1465 cm−1
1228 cm−1 1145 cm−1 1396 cm−1 1423 cm−1
1182 cm−1 1114 cm−1 1367 cm−1 1390 cm−1
1114 cm−1 1101 cm−1 1338 cm−1 1367 cm−1
1101 cm−1 1087 cm−1 1268 cm−1 1338 cm−1
1089 cm−1 1074 cm−1 1222 cm−1 1267 cm−1
1070 cm−1 1055 cm−1 1199 cm−1 1220 cm−1
1053 cm−1 1041 cm−1 1164 cm−1 1164 cm−1
1041 cm−1 1028 cm−1 1145 cm−1 1145 cm−1
1028 cm−1 1010 cm−1 1114 cm−1 1114 cm−1
1001 cm−1 993 cm−1 1101 cm−1 1101 cm−1
981 cm−1 981 cm−1 1089 cm−1 1087 cm−1
960 cm−1 960 cm−1 1074 cm−1 1068 cm−1
937 cm−1 914 cm−1 1056 cm−1 1056 cm−1
914 cm−1 889 cm−1 1043 cm−1 1041 cm−1
875 cm−1 875 cm−1 1012 cm−1 1018 cm−1
860 cm−1 860 cm−1 993 cm−1 993 cm−1
Despite the fact that the tomato samples absorb less than the apples,
large differences are observed between the four cultivars. Aranca absorbs
more light than the other cultivars in the whole wavenumber range studied.
The largest differences are found in the area between 1200 cm−1 and 900
cm−1, which is the main area of interest for sugars and acids because of
the strong C-O stretching vibrations. Aranca shows higher absorption as
a result of the high content of three sugars in this cultivar. The results of
the HPLC measurements show that Aranca contains high concentrations of
sucrose, glucose and fructose. Clotilde shows higher absorbance than the
Fourier transform infrared spectroscopy 119
other two cultivars in the range between 1458 cm−1 and 885 cm−1. The
HPLC data confirm that Clotilde has the second highest content of glucose
and fructose. The content of both sugars in Clotilde is higher than that in
Climaks and DRW 73-29. From the first derivative absorbance spectrum,
20 wavenumbers were selected for further data analysis (Table 4.9).
0 000
0.002
0.004
0.006
0.008
PC
2
PC 1
Apple
Tomato
-0.008
-0.006
-0.004
-0.002
0.000-0.015 -0.010 -0.005 0.000 0.005 0.010 0.015
Figure 4.7: Score plot of the PLS-DA of the apple and tomato samples measured
using ATR-FTIR (X-expl. (79%, 15%); Y-expl. (96%, 1%)).
A PLS-DA was performed on the correlation matrix of the data from
the ATR-FTIR analysis to classify the apple and tomato cultivars based
on their absorbance spectra. The apple and tomato samples are separated
from each other in the score plot (Figure 4.7). The variability within the
apple samples is larger than that within the tomato samples. The tomatoes
are positioned in a line. This line, however, does not represent a time shift
of the instrument detector but a shift according to the cultivar. The line
occurs due to the fact that a large variability is present in the apple samples
120 4.3 Results
compared to the tomato samples. Within each group, apples and tomatoes,
the samples are grouped per cultivar. Using ATR-FTIR it is thus possible
to discriminate between species.
The results of the PLS-DA performed on the apple data are presented
in Figure 4.8. Cox and Elstar are clearly separated from each other and the
other apple cultivars along the axis of PC 1. Both cultivars are located in
the left quadrants of the plot. In the correlation loadings plot two ellipses
are visible. The inner and outer ellipses on the figures represent correla-
tion coefficients of 70% and 100% (or R2 values of 50% and 100%). For a
wavenumber located between the two ellipses more than 70% of its variabil-
ity is explained by the first two PC’s. This means this variable is important
in describing the variability, and, hence, the major cultivar effects, within
the data set. The classification along the axis of PC 1 is mainly caused by
wavenumbers 1271 cm−1, 1182 cm−1, 1115 cm−1, 1101 cm−1, 1090 cm−1,
1042 cm−1, 1001 cm−1, 982 cm−1 and 914 cm−1 in the first derivative spec-
tra. Cox is highly correlated with these wavenumbers. They are all situated
in the region of strong C-O stretching vibrations of sugars in the absorbance
spectrum. As mentioned before, Cox and Elstar show high absorptions in
the region between 1200 cm−1 and 900 cm−1. Golden and Pinova are posi-
tioned in the upper and lower right quadrants of the score plot, respectively,
but do both overlap with the Jonagold samples. Pinova shows high ab-
sorbances around 1100 cm−1. In this region C-O vibrations occur, which
are related to a high content of glucose and fructose in this cultivar. Pinova
is correlated to 1229 cm−1, 1053 cm−1, 1028 cm−1 and 961 cm−1. From
the PLS-DA correlation loadings can be observed that the vibrations in the
region related to sugar content are very important for the separation be-
tween Cox and Pinova. Similar differences in sucrose content were found
in the HPLC data. The classification results (Table 4.10) show that using
ATR-FTIR all samples of these two cultivars are classified correctly. The
results of the analysis with ATR-FTIR differ a lot from those of the ETSPU.
Using the multisensor system, it was not possible to classify the samples by
cultivar and to distinguish between the apple cultivars except for Pinova.
Fourier transform infrared spectroscopy 121
-0.004
-0.002
0.000
0.002
0.004
-0.006 -0.004 -0.002 0.000 0.002 0.004 0.006P
C 2
PC 1
CoxElstarGoldenJonagoldPinova
A
876937
1070
1090
1101
1115
1182
1271 1449
1468
Elstar
Golden
Jonagold0.2
0.4
0.6
0.8
1.0
PC
2
860914
961982
1001
1028
10421053
1101
1229
1400
CoxPinova
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC 1
B
Figure 4.8: Score plot (A) and correlation loadings plot (B) of the PLS-DA of
the apple samples measured by ATR-FTIR (X-expl. (79%, 16%); Y-expl. (22%,
10%)).
122 4.3 Results
Table 4.10: Classification results of the PLS-DA performed on the reference data
and ATR-FTIR measurements on apple and tomato. The percentage of correct
classified samples is shown (%).
Cultivar HPLC ATR-FTIR
Cox 100 100
Elstar 75 90
Jonagold 85 70
Golden 35 80
Pinova 60 100
Aranca 100 95
Climaks 90 95
Clotilde 65 60
DRW 73-29 85 80
A similar analysis was carried out on the tomato samples. The results
are visualized in Figure 4.9. Aranca is clearly separated from the other three
cultivars along the axis of the first PC, which is the same result as found
using the ETSPU. Using a supervised method to classify the cultivars, Cli-
maks and DRW 73-29 are separated from each other along the axis of the
second PC. According to the correlation loadings this separation is caused
by wavenumber 1074 cm−1. Clotilde is positioned in between both previ-
ously mentioned cultivars along the axis of the second PC. The absorption
at this wavenumber is an important indication of the fructose content of
the samples. The absorbance spectra of Climaks and DRW 73-29, however,
show complete overlap in the region around this wavenumber while the ref-
erence measurements indicate differences in fructose content between both
cultivars. Climaks, Clotilde and DRW 73-29 are not directly correlated to
any of the selected wavenumbers. Using the ETSPU, Climaks, Clotilde and
DRW 73-29 are not separated from each other. This is also reflected in the
percentage of correctly classified samples. Using ATR-FTIR, respectively
95%, 60% and 80% of the samples of Climaks, Clotilde and DRW 73-29 are
classified correctly (Table 4.10). While, as shown in Chapter 3, only 60%,
15% and 10% of the same samples are classified within the correct cultivar
using the ETSPU.
Fourier transform infrared spectroscopy 123
-0.0003
-0.0002
-0.0001
0.0000
0.0001
0.0002
0.0003
-0.003 -0.002 -0.001 0.000 0.001 0.002 0.003 0.004
PC
2PC 1
Aranca
Climaks
Clotilde
DRW 73-29
A
860876
889
914961
982
993
1011
1028
1042
1055
1074
1088
1101
1115
11461165 1223
1269
1310Aranca
Climaks
Clotilde
DRW 73-29
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC
2
PC 1
B
Figure 4.9: Score plot (A) and correlation loadings plot (B) of the PLS-DA of
the tomato samples measured by ATR-FTIR (X-expl. (98%, 0%); Y-expl. (28%,
23%)).
124 4.3 Results
4.3.2.2 Quantification with ATR-FTIR
A PLS2 analysis was performed based on the ATR-FTIR spectra of both
the apple and tomato samples. Table 4.11 presents the results of the PLS2
prediction model for the tomato samples. The results of the prediction
models of apple are not shown because of their similarity to the results
found for tomato. Based on the RMSEC values, the LOD was calculated
for each compound in the tomato matrix. Table 4.12 shows that all LOD’s
are below the values found in the tomato samples, which indicates that all
compounds can be detected using this method. The correlations between
the compounds present in the apple and tomato samples and the ATR-FTIR
absorbance spectra are low. For tomato, the ’best’ PLS models are made for
malic acid, glucose and fructose. The correlations of the calibration models
are respectively 88%, 89% and 88% for malic acid, glucose and fructose.
Both the calibration and validation model of all three compounds are very
similar. The RMSEC and RMSECV values of calibration and validation
model for malic acid are very low. Despite this, the RPD value of the
malic acid model is low. While an RPD value below 1.5 indicates that the
model is not usable, an RPD value between 1.5 and 2 reveals a possibility
to distinguish between high and low values. The models for glucose and
fructose have RPD values of respectively 2.1 and 2.1, which indicates that
approximate predictions are possible for these sugars.
4.3.3 Extracted samples versus juices
4.3.3.1 Classification with EHT
To introduce a larger range in sugar and acid content in the samples, shelf
life was introduced in this experiment. No significant differences, however,
were found between the samples which were stored for one day and one
week at ambient atmosphere (results not shown). This is contradictory to
storage physiology, where the content of sugars should increase during shelf
life. A shelf life of one week, however, does not introduce singificant changes
in sugar and acid profile. The effect of shelf life will therefore be discarded
Fourier transform infrared spectroscopy 125
Table 4.11: PLS2 models to predict individual compounds measured by HPLC
in tomato samples built on the results of the ATR-FTIR measurements. Cross-
validation was used to validate the model. The offsets, RMSEC and RMSECV
values are given in mg/g powder.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Malic acid Calibration 0.78 0.02 0.88 0.03
Validation 0.76 0.02 0.86 0.03 1.8
Citric acid Calibration 0.49 0.80 0.70 0.28
Validation 0.42 0.90 0.61 0.31 1.3
Sucrose Calibration 0.49 0.29 0.70 0.14
Validation 0.47 0.31 0.67 0.15 1.4
Glucose Calibration 0.80 0.32 0.89 0.19
Validation 0.77 0.36 0.87 0.21 2.1
Fructose Calibration 0.78 0.33 0.88 0.15
Validation 0.76 0.37 0.86 0.17 2.1
Table 4.12: Limit of detection (mg/g powder) of taste compounds in a tomato
matrix.
Compound LOD
Fructose 0.29
Glucose 0.37
Sucrose 0.27
Citric acid 0.55
Malic acid 0.06
126 4.3 Results
in the rest of the analysis. The EHT measurements of the extracted and
juiced tomato samples give similar results of the content of taste compounds.
Bonaparte contains high amounts of both sugars and malic acid. The malic
acid content is higher than that of the other cultivars. The content of citric
acid in this cultivar is lower than that of the Clotilde. Tricia contains the
lowest concentrations of glucose and fructose.
Fructose
Citric acidClotilde
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1Glucose
Malic acid
Glutamate
Bonaparte
Tricia
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC 1
Figure 4.10: Correlation loadings plot of the PLS-DA of the extracted tomato
samples measured by EHT (X-expl. (91%, 6%); Y-expl. (37%, 41%)).
A PLS-DA was performed on the data of the EHT analysis of both sam-
ple preparations. Figure 4.10 shows the results for the extracted samples.
The separation of the three tomato cultivars appears along the axes of the
first two PC’s. In the correlation loadings plot shows that Bonaparte is
correlated with malic acid, while Clotilde is correlated with citric acid and
Tricia is negatively correlated with glucose, fructose and glutamate. The
same PLS-DA performed on the EHT data of the juices (Figure 4.11) shows
similar results. The results of the reference measurements for both sample
preparation techniques are similar. It can be concluded that both the ex-
tracts and juices might be used for reference analysis (Vermeir et al., 2007).
Fourier transform infrared spectroscopy 127
Since the juices demand less sample preparation, in future experiments juices
will be used.
Glucose
Fructose
Malic acid
Citric acid
Glutamate
Bonaparte
Clotilde
Tricia
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC
2
PC 1
Figure 4.11: Correlation loadings plot of the PLS-DA of the tomato juices mea-
sured by EHT (X-expl. (91%, 6%); Y-expl. (36%, 22%).
4.3.3.2 Classification with ATR-FTIR
The average absorbance spectra of the extracts and juices of the three tomato
cultivars are given in Figure 4.12. The absorbance spectra of the extracts
are lower than those for the juices. This means a large amount of chemical
compounds is lost during preparation of the extracts. The lower absorbances
also result from the dilution which occurs during the preparation of the
extracts. Tricia absorbs less than the other two cultivars, both as a juice
and an extract, due to its low content of glucose, fructose, malic acid and
glutamate.
128 4.3 Results
0.00
0.02
0.04
0.06
0.08
0.10
0.12
1797
1751
1705
1659
1612
1566
1520
1473
1427
1381
1335
1288
1242
1196
1149
1103
1057
1011 964
918
Abs
orba
nce
Wavenumber (cm-1)
Tricia-juiceTricia-extractBonaparte-juiceBonaparte-extractClotilde-juiceClotilde-extract
Figure 4.12: Average ATR-FTIR absorbance spectra of three tomato cultivars
with two types of sample preparation.
Twenty wavenumbers were selected from the first derivative absorbance
spectra for further data analysis. The selected wavenumbers are the same
for both the extracted samples and the juices (Table 4.9). Compared to the
previous experiment, more wavenumbers in the region of CH2 scissoring, CH
bending and OH bending were chosen. This can be explained by the fact
that less noise is present in the spectra compared to experiment 2. Different
FTIR spectrometers, horizontal ATR crystals and number of co-added scans
were used for both experiments (Table 4.3). The selected wavenumbers in
the region with high absorption between 1200 cm−1 and 990 cm−1 are similar
in both experiments.
Fourier transform infrared spectroscopy 129
-0.0003
-0.0002
-0.0001
0.0000
0.0001
0.0002
0.0003
0.0004
-0.0004 -0.0003 -0.0002 -0.0001 0.0000 0.0001 0.0002 0.0003 0.0004
PC
2PC 1
Tricia
Bonaparte
Clotilde
Figure 4.13: Score plot of the PLS-DA of the extracted tomato samples measured
by ATR-FTIR (X-expl. (15% 13%); Y-expl. (31%, 11%)).
Table 4.13: Classification results of the PLS-DA performed on the reference data
and ATR-FTIR measurements on the tomato extracts and juices. The percentage
of correct classified samples is shown (%).
Cultivar EHT ATR-FTIR
Extract Juice Extract Juice
Bonaparte 100 100 100 100
Clotilde 90 70 70 90
Tricia 100 80 80 90
130 4.3 Results
-0.0005
-0.0003
-0.0001
0.0001
0.0003
0.0005
-0.002 -0.001 0.000 0.001 0.002
PC
2
PC 1
Tricia
Bonaparte
Clotilde
Figure 4.14: Score plot of the PLS-DA of the tomato juices measured by ATR-
FTIR (X-expl. (84%, 3%); Y-expl. (42%, 37%)).
The PLS-DA performed on the absorbances at the 20 selected wavenum-
bers of the ATR-FTIR measurements of the extracted samples (Figure 4.13)
shows that the samples of the three cultivars overlap quite a lot. Tricia and
Bonaparte can be separated from each other along the axis of PC 1, but
Clotilde shows overlap with both cultivars. No strong correlations are found
between the selected wavenumbers and the tomato cultivars. The classifi-
cation results, however, show that respectively 100%, 70% and 80% of all
samples are classified correctly within Bonaparte, Clotilde and Tricia. The
results of the same analysis performed on the ATR-FTIR data of the juices
using 20 selected wavenumbers are shown in Figure 4.14. In contrast to the
extracts, all three cultivars are separated from each other and 90% to 100%
of the samples are classified correctly (Table 4.13). Tricia has positive PC 1
scores, while the other two cultivars have negative PC 1 scores. Bonaparte
Fourier transform infrared spectroscopy 131
and Clotilde are again separated from each other along PC 2. Tricia is cor-
related with wavenumbers 1466 cm−1, 1425 cm−1, 1396 cm−1, 1338 cm−1,
1165 cm−1 1146 cm−1, 1115 cm−1, 1090 cm−1 and 1012 cm−1. Bonaparte
and Clotilde are not correlated with any of the selected wavenumbers. This
analysis shows that large amounts of chemical information which is impor-
tant for the classification were lost during the extraction. While the juices
are grouped easily based on their chemical content, this is not possible for
the extracted samples.
Glucose
Malic acid
Glutamate
1425
1115
11011090
10741057
10431012
Bonaparte
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
Fructose
Citric acid
1743
1466
144814251396
136713381269
1223
12001165
11461012
993Tricia
Clotilde
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Figure 4.15: Correlation loadings plot of the PLS-DA of the tomato juices mea-
sured by EHT and ATR-FTIR (X-expl. (84%, 3%); Y-expl. (56%, 10%)).
To correlate the results of the reference analysis performed on juices to
the ATR-FTIR results, a PLS-DA was performed on both data sets together.
Figure 4.15 shows the correlation loadings plot indicating the cultivars, taste
compounds and wavenumbers. All of the 20 selected wavenumbers, except
for 1743 cm−1, are positively correlated with both glucose and fructose and
132 4.3 Results
negatively correlated with Tricia. This implies that Tricia contains low
contents of glucose and fructose, which is reflected in a low absorbance. As
shown in Figure 4.1, wavenumber 1743 cm−1 is located on the flank of the
large peak at 1722 cm−1. This peak is the result of high absorption of IR
light by C=O bonds in citric and malic acid. In the sugar spectra, there is
no peak at this wavenumber, indicating why there is no correlation between
1743 cm−1 and the two sugars.
4.3.3.3 Quantification with ATR-FTIR
The main PLS2 results of both the extracts and juices are shown in Table
4.14. Despite the fact that exactly the same samples were analyzed us-
ing ATR-FTIR and the reference technique, the calibration and validation
models of the extracts and juices do not show any possibility of ATR-FTIR
to predict the chemical composition of the samples. The correlations be-
tween the compounds present in the tomato samples and the ATR-FTIR
absorbance spectra are low for all analyses. Even though the models built
on the extracted samples report the highest correlations, the RMSECV and
RPD values are not satisfactory. The validation models for citric acid in
both the extracts and juices show negative correlations. The RPD values of
all compounds analyzed both as extracts and juices are lower than 2, indi-
cating that only high and low values can be distinguished. An explanation
for the bad prediction models is found in the fact that the three tomato
cultivars only cover a very small concentration range. The range of glu-
cose present in the juices is the largest and goes from 9.8 g/L to 14.7 g/L.
In a next experiment the range of concentrations of all compounds will be
broadened using a dilution of the samples and a standard addition of two
mixtures to the samples.
Fourier transform infrared spectroscopy 133
Table 4.14: Correlations, RMSECV and RPD values found in the PLS2 models
to predict individual compounds measured by EHT in tomato extracts (mg/g) and
juices (g/L) built on the results of the ATR-FTIR measurements. Cross-validation
was used to validate the model.
Compound Extract Juice
Malic acid R 0.10 0.30
RMSECV 0.34 0.28
RPD 1.0 1.1
Citric acid R -0.31 -0.54
RMSECV 0.67 0.53
RPD 1.0 1.0
Glutamate R 0.71 0.56
RMSECV 0.27 0.33
RPD 1.4 1.2
Glucose R 0.80 0.75
RMSECV 1.25 1.34
RPD 1.7 1.6
Fructose R 0.76 0.64
RMSECV 1.08 1.09
RPD 1.6 1.5
4.3.4 Dilutions and standard additions
The absorbance spectrum of Loredana is shown together with the spectra
of the diluted sample and samples with addition of mixture 1 and mixture
2 in Figure 4.16. The original sample shows a higher absorption than the
diluted sample in the whole spectral region studied. The spectra of the
samples with an addition of mixture 1 and mixture 2 have the same shape.
The inconsistency in the spectra around 1600 cm−1 comes from differences
in the water content of the presented samples.
134 4.3 Results
Table 4.15: Average results of EHT measurements performed on the original
tomato samples (average ± standard deviation, concentrations in g/L).
Cultivar Glucose Fructose Sucrose Citric acid Malic acid
Macarena 8±1 24±4 4±1 4±3 0.5±0.1
Growdena 7±1 25±7 4±1 3±1 0.53±0.08
Tricia 7±2 24±5 5±3 4±2 0.4±0.1
Admiro 6±1 21±5 4±2 5±2 0.5±0.1
Loredana 5±1 21±5 3±3 5±3 0.8±0.9
Cherry tomato 13±2 39±10 8±2 7±2 0.5±0.1
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
1799
1753
1707
1660
1614
1568
1522
1475
1429
1383
1336
1290
1244
1198
1151
1105
1059
1012 966
920
Abs
orba
nce
Wavenumber (cm-1)
DilutionAddition of mix 1Addition of mix 2Original
Figure 4.16: Average ATR-FTIR absorbance spectra of Loredana: diluted sample,
original sample and samples with standard additions.
A PLS2 analysis was performed on the samples analyzed using ATR-
FTIR. The absorbances at 20 selected wavenumbers were used in the anal-
ysis (Table 4.9). The diluted samples, the original samples and the samples
Fourier transform infrared spectroscopy 135
with an addition of mixture 1 and mixture 2 were included in the PLS2
models to increase the range of chemical components. The results of the
prediction models are presented in Table 4.16. Due to the large concentra-
tion range present in the samples, acceptable prediction models are made
with ATR-FTIR. High correlations are observed in the calibration models
of almost all compounds, with values above 90% for glucose, fructose and
malic acid. Despite the high correlations between the measured and the pre-
dicted concentrations, high offsets, RMSEC and RMSECV values are found.
The RPD values of all three compounds are higher than 2, with values of
respectively 3.1, 2.3 and 2.2 for glucose, fructose and malic acid. With a
correlation of 86% the prediction model of citric acid still has a RPD value
of 2.
Table 4.16: PLS2 models to predict individual compounds measured by EHT in
tomato samples built on the results of the ATR-FTIR measurements of the diluted
samples, original samples and samples with a standard addition. Cross-validation
was used to validate the model. The offsets, RMSEC and RMSECV values are
given in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Malic acid Calibration 0.81 0.27 0.90 0.50
Validation 0.81 0.28 0.89 0.51 2.2
Citric acid Calibration 0.75 1.63 0.86 1.63
Validation 0.74 1.68 0.86 1.67 2.0
Sucrose Calibration 0.71 1.63 0.84 2.03
Validation 0.70 1.71 0.83 2.10 1.7
Glucose Calibration 0.90 0.79 0.95 1.12
Validation 0.90 0.82 0.95 1.15 3.1
Fructose Calibration 0.81 4.09 0.91 4.31
Validation 0.81 4.29 0.90 4.45 2.3
4.3.5 Quality control of fruit juices
The multifruit juices, syrups and mixtures, which were analyzed with the
ETSPU, were also analyzed with ATR-FTIR. Figure 4.17 shows the ab-
136 4.3 Results
sorbance spectra of the nine syrups. Large differences are observed in the
absorbance spectra. The absorbance spectrum of lemon syrup differs greatly
from the absorbance spectra of the other syrups. Based on the absorption
spectrum of the lemon syrup is found that this syrup contains high amounts
of C=O bonds, which are present in acids, and low amounts of C-O bonds,
which are mainly present in sugars. Orange syrup and blend-9 fruit syrup
both absorb highly in the spectral area between 1004 cm−1 and 987 cm−1.
The apple syrup absorbs the most IR light of all syrups between 1081 cm−1
and 993 cm−1 which is caused by the absorption of IR light by the C-O
bonds of the sugars present in this syrup.
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
1797
1750
1704
1658
1612
1565
1519
1473
1426
1380
1334
1288
1241
1195
1149
1103
1056
1010 964
918
Abs
orba
nce
Wavenumber (cm-1)
Blend-9 fruit LemonOrange Passion fruitApple Red grapeElderberry CherryStrawberry
Figure 4.17: Average ATR-FTIR absorbance spectra of nine fruit syrups.
The absorbance spectra of elderberry syrup and red grape syrup have a
very similar shape and show some resemblance to the spectra of strawberry
syrup and cherry syrup. Elderberry syrup, however, absorbs more light than
any other syrups around 1581 cm−1.
Fourier transform infrared spectroscopy 137
The differences in the absorbance spectra of the syrups are reflected in
the PCA. The nine syrups and 11 mixtures produced from the individual
syrups (Table 3.4) were analyzed using PCA (Figure 4.18). The full first
derivative spectra were used in the analysis since the differences in chemical
composition make it impossible to select 20 wavenumbers which are repre-
sentative for the absorbance spectrum of all the syrups. Lemon syrup is
separated from the other syrups and mixtures. This separation is caused
by the very different absorbance spectrum of lemon syrup compared to the
other syrups.
0.2
0.3
0.4
PC
2Blend-9 fruit LemonOrange Passion fruitApple Red grapeElderberry CherryStrawberry Mix 1Mix 2 Mix 3Mix 4 Mix 5Mix 6 Mix 7Mix 8 Mix 9Mix 10 Mix 11
-0.2
-0.1
0.0
0.1
-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4
PC 1
Figure 4.18: Score plot of the PCA of the 9 syrups and 11 mixtures of syrups
measured by ATR-FTIR (X-expl. (52%, 27%)).
This classification is quite different from that of the ETSPU analysis.
Blend-9 fruit syrup and orange syrup are located closely to each other and
mixtures 4, 6, 7 and 8 and the mixture resembling Ace (mixture 1). All
of the mixtures, except for mixture 8, contain high concentrations of both
138 4.3 Results
blend-9 fruit and orange syrup, which explains the close position on the
score plot. Elderberry syrup, red grape syrup, strawberry syrup and cherry
syrup are classified together with four mixtures which are produced out of
these syrups. The grouping of the four syrups is not retrieved when analyz-
ing the samples with the ETSPU, where they are classified into two groups,
surrounding the mixtures that contain these syrups. The apple syrup, fi-
nally, is positioned closely to two mixtures which contain high contents of
apple syrup. After performing the analysis without lemon syrup, similar re-
sults are found (not shown). Syrups with similar composition and mixtures
containing the same syrups are positioned together in the score plot of a
PCA.
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
1797
1750
1704
1658
1612
1565
1519
1473
1426
1380
1334
1288
1241
1195
1149
1103
1056
1010 964
918
Abs
orba
nce
Wavenumber (cm-1)
Ace
Benefits Vitality
Benefits Immunity
Figure 4.19: Average ATR-FTIR absorbance spectra of the three multifruit juices.
The absorbance spectra of the three multifruit juices, Ace, Benefits Vi-
tality and Benefits Immunity, are shown in Figure 4.19. The multifruit juices
absorb less IR light than the syrups they are made of. An explanation for
Fourier transform infrared spectroscopy 139
this is found in the composition of the multifruit juices. All three juices
contain amounts of extra compounds next to the syrups (Table 3.3). The
spectra of Benefits Vitality and Benefits Immunity are very similar, despite
their differences in composition. The multifruit juice Ace absorbs less IR
light than the other two juices due to the lower fruit syrup content and the
presence of water in this juice.
The three mixtures that were made based on information of the fruit
content of the three multifruit juices were analyzed together with the three
multifruit juices (Figure 4.20). The three multifruit juices are clearly sepa-
rated from the three mixtures along PC 1, however, the same trend is visible
in the two groups of samples. More information and absorbance spectra of
the extra components (aloe vera puree, minerals, vitamins) present in the
multifruit juices are necessary to make a complete classification model based
on the fruit content.
-0.20
-0.10
0.00
0.10
0.20
-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30
PC
2
PC 1AceBenefits Vitality
Benefits Immunity
Mix Ace
Mix Benefits Vitality
Mix Benefits Immunity
Figure 4.20: Score plot of the PCA of the three multifruit juices and the mixtures
with the same fruit content measured by ATR-FTIR (X-expl. (76%, 14%)).
140 4.3 Results
The possibility to predict the fruit syrup content in the three multifruit
juices using ATR-FTIR was studied in a PLS2 analysis. The results are
shown in Table 4.17. The PLS2 model gives good results for all nine syrups,
with high slopes and correlations and low offsets and errors. Also, low
RMSEC and RMSECV values are found, especially for lemon, passion fruit,
cherry and strawberry with values lower than 0.10.
Table 4.17: PLS2 model to predict individual syrups in multifruit juices based
on the ATR-FTIR spectra. Cross-validation was used to validate the model. The
offsets, RMSEC and RMSECV values are given in % v/v
Syrup Slope Offset Correlation RMSEC
RMSECV
Blend-9 fruit Calibration 0.99 0.001 0.99 0.11
Validation 0.99 0.02 0.99 0.15
Lemon Calibration 0.99 0.001 0.99 0.01
Validation 0.99 0.001 0.99 0.01
Orange Calibration 0.99 0.01 0.99 0.51
Validation 0.99 0.13 0.99 0.68
Passion fruit Calibration 0.99 0.001 0.99 0.04
Validation 0.99 0.01 0.99 0.05
Apple Calibration 0.99 0.001 0.99 0.22
Validation 0.99 0.03 0.99 0.29
Red grape Calibration 0.99 0.001 0.99 0.20
Validation 0.99 0.06 0.99 0.27
Elderberry Calibration 0.99 0.001 0.99 0.12
Validation 0.99 0.03 0.99 0.16
Cherry Calibration 0.99 0.001 0.99 0.03
Validation 0.99 0.01 0.99 0.04
Strawberry Calibration 0.99 0.001 0.99 0.04
Validation 0.99 0.01 0.99 0.06
In addition to this prediction, the ability of ATR-FTIR to detect small
differences in the syrup content of the multifruit juices is evaluated. Hereto,
different amounts of passion fruit and cherry syrup are added to respectively
Benefits Vitality and Benefits Immunity. Figure 4.21 illustrates the changes
Fourier transform infrared spectroscopy 141
which occur in the absorption spectrum of Benefits Immunity due to the
addition of cherry syrup. The total amount of absorbed light increases
because (i) by adding syrup to the multifruit juice, the samples become
less diluted and thus absorb more; (ii) the syrup is highly concentrated
and, thus, contains large amounts of sugars and acids, causing more light
to be absorbed. A PLS1 analysis was performed on the data to determine
whether additions of syrup in a multifruit juice can be detected using ATR-
FTIR (Table 4.18). Both the calibration and validation model of cherry and
passion fruit syrup have high correlations and low RMSE values. The high
RPD values indicate that both syrups are traceable and can be predicted
excellently. This proves that ATR-FTIR is very accurate to predict small
amounts in a complex matrix.
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1797
1751
1705
1659
1612
1566
1520
1473
1427
1381
1335
1288
1242
1196
1149
1103
1057
1011 964
918
Abs
orba
nce
Wavenumber (cm-1)
10:09:18:27:36:45:5
Figure 4.21: ATR-FTIR absorbance spectra of additions of cherry syrups to
Benefits Immunity. The multifruit juice:syrup ratio is indicated.
142 4.4 Discussion
Table 4.18: Main validation results of PLS1 model using cross-validation to pre-
dict added cherry and passion fruit syrup in two multifruit juices based on the
ATR-FTIR spectra (% v/v).
Syrup Correlation RMSECV RPD
Passion fruit 0.99 0.12 15
Cherry 0.99 0.27 6.4
4.4 Discussion
4.4.1 Classification of samples
The absorbance spectra of the taste compounds that are important for fruit
were collected using ATR-FTIR. Glucose, fructose, sucrose, citric acid and
malic acid were analyzed. Large differences between the absorbance spectra
of the three sugars and the two acids are found. Two vibrations are mainly
responsible for these large differences. C=O stretching vibrations at 1722
cm−1 are present in the spectra of both acids. These vibrations are very
specific for acid recognition based on absorbance spectra. C-O vibrations
between 1100 cm−1 and 950 cm−1, on the other hand, are mainly present in
the spectra of the three sugars. The importance of this vibration was also
found by Back et al. (1984) and De Lene Mirouze et al. (1993) while studying
carbohydrates in aqueous solutions and glucose syrups, respectively. Sucrose
has, due to its specific chemical structure, an absorbance spectrum with large
differences between glucose and fructose. The high absorbances at 997 cm−1
and 923 cm−1 make it possible to recognize the presence of sucrose in spectra
of sugars, which was also described by Irudayaraj and Tewari (2003).
The ability of ATR-FTIR to classify apple and tomato cultivars based on
their taste compounds was studied in a second experiment. Like the ETSPU,
ATR-FTIR is able to discriminate between apples and tomatoes. This seems
very plausible. Nevertheless, similar taste compounds are present in both
fruit. While the ET was only able to discriminate between very different cul-
tivars, ATR-FTIR can classify the apple and tomato cultivars more clearly.
The differences in the absorbance spectra of the five apple cultivars and
Fourier transform infrared spectroscopy 143
four tomato cultivars are related to differences in the chemical composition
of the fruit. High absorbances between 1100 cm−1 and 900 cm−1 are mainly
assigned to C-O stretching vibrations of glucose, fructose and also sucrose
in apple. Cultivars with high absorbances in this region of wavenumbers,
contain a high content of one or more of the studied sugars. The possibility
of ATR-FTIR to correlate the absorbances at specific wavenumbers to chem-
ical compounds is a large advantage of this technique over the ET, which
rather follows a black box approach. In this experiment, the analysis of
the reference measurements was performed on extracted apple and tomato
samples. The ATR-FTIR measurements, on the other hand, were carried
out on juices. This discrepancy was looked at in a next experiment.
Two types of sample preparation were evaluated into detail. The po-
tential of EHT, as a reference technique, and ATR-FTIR to discriminate
between cultivars was used to determine which sample preparation tech-
nique should be used for rapid analysis of taste compounds. The results of
the analysis with EHT are very similar for both the extracts and juices. This
means that no crucial information on the taste compounds gets lost during
the extraction process. When analyzing the same samples with ATR-FTIR,
however, different results are found for both sample preparation techniques.
The absorbance spectra of the extracted samples contain a lot less informa-
tion on the chemical composition than the spectra of the juices. Clearly,
during the extraction process some chemical information is lost. From the
PLS-DA can be concluded that the discrimination of samples using ATR-
FTIR is based on matrix constituents other than just the carbohydrates and
organic acids. To minimize sample preparation time, the analysis with both
the reference technique and ATR-FTIR should be performed on juices. This
was also stated by Irudayaraj and Tewari (2003). Garrigues et al. (2000)
found that a minimal sample preparation resulted in the most accurate re-
sults, since extensive sample preparation requires perfect repeatability and
can lead to a large variability in absorbance.
144 4.4 Discussion
4.4.2 Quantification of taste compounds
A dilution series and mixtures of pure taste compounds were analyzed us-
ing ATR-FTIR. The results showed there is a linear relation between the
concentration of a compound and its absorption spectrum and that spectra
can be added to each other as a result of Beer’s Law (Chalmers and Grif-
fiths, 2002). Prediction models based on the individual compounds were
made using different selection processes for the variables. A selection of
wavenumbers was also proposed by Kelly and Downey (2005), Irudayaraj
and Tewari (2003), Garrigues et al. (2000) and Vonach et al. (1998) to re-
duce the size of the data matrix and shorten analysis time. The PLS results
showed that ATR-FTIR is able to predict the concentration of all studied
compounds correctly using only two selected wavenumbers. Garrigues et al.
(2000), however, found that the use of a selected wavenumber is not enough
to predict the content of sucrose in a complex mixture due to interferences.
It seems necessary to look at the absorbances at more wavenumbers in or-
der to verify changes in the sugar bands. This was also found in the results
presented in this thesis. By selecting the minima and/or maxima of the
first derivative absorbance spectra, the main vibrations of the studied com-
pounds are used for further data analysis. The PLS models indicated that
this is the most optimal selection procedure. From this experiment can be
concluded that ATR-FTIR is able to predict taste compounds in mixtures
if their concentration is above the determined LOD values.
In a second experiment, the main taste compounds of apple and tomato
were predicted using ATR-FTIR. Despite the fact that the PLS2 models are
better than those described in Chapter 3, the models give results which are
not satisfactory. There are two possible explanations for the bad results.
First, as mentioned in Chapter 3, the reference measurements performed on
these samples were performed using HPLC and, thus, an extensive sample
pretreatment was needed for the reference analysis. The measurements with
ATR-FTIR and HPLC were performed on samples which had undergone
different samples treatments. Second, since the LOD’s indicate that all
compounds could be detected in the samples, the concentration ranges of
Fourier transform infrared spectroscopy 145
the taste compounds and the tomato matrix would be expected to influence
the predictions.
In a third experiment two sample preparation techniques were compared.
Both extracts and juices were used for the reference analysis and the ATR-
FTIR measurements. Since the PLS2 analysis shows similar results for the
extracts and juices, samples could be analyzed by EHT and ATR-FTIR
with a minimal sample preparation. Irudayaraj and Tewari (2003) reported
that this indicates that ATR-FTIR has potential as a routine procedure for
the quantification of multiple constituents without any sample preparation
in quality control. In this experiment, however, the prediction models for
all taste compounds in the juices are poor, with low correlations and RPD
values. This indicates the importance of the concentration ranges and the
matrix. Schindler et al. (1998a) observed the same problem in the analysis
of wine samples. His prediction models for tartaric acid and acetic acid,
which are present in small concentration ranges, showed low correlations
between the measured and predicted values in model solutions.
The ability of ATR-FTIR to predict taste compounds was studied further
using standard additions of taste compounds. In a fourth experiment the
range of concentrations of all compounds was broadened using a dilution
of the samples and a standard addition of two mixtures to the samples.
The PLS2 models resulting from the additions to the tomato samples give
far better results than the previous experiments confirming the importance
of the matrix. RPD values close to and higher than 2 are found for all
compounds, indicating that all compounds can be predicted (Saeys et al.,
2005).
To improve the quantification rate of taste compounds using ATR-FTIR,
without adding an extensive separation of the compound of interest, sev-
eral statistical techniques could come to aid. These techniques deal with
the problem of finding a representative calibration set for the prediction
model. A representative calibration set is not so easy to collect, because
not only the expected variation in the compounds of interest should be in-
cluded, but also the variation of the other contributing factors, interferents,
146 4.4 Discussion
and their correlations with each other and with the compounds of interest.
Many researchers have investigated methodologies to increase the robust-
ness of prediction models against changes in the interferent structure. A
first approach aims to remove the influence of these interferents by means of
a pre-processing of the data, like extended multiplicative signal correction
(EMSC), pre-whitening and physics-based multiplicative scatter correction
(MSC). A second approach tries to expand the calibration set in order to in-
clude all variation. Recently, a new class of multivariate calibration methods
called augmented classical least squares (ACLS) has been proposed which
is an extension of the classical least squares model (CLS) to handle cases
where not all compounds contributing to the absorbance signal are explicitly
included in the calibration models (Martens and Stark, 1991; Martens and
Naes, 1998; Saeys et al., 2008).
4.4.3 Quality control of fruit juices
The potential of ATR-FTIR to be used as a tool for quality control was eval-
uated using multifruit juices. Errors in the production process of all food
products should be detected rapidly. The classification of the syrups and
mixtures with PCA is clearly based on the fruit syrup content. Fruits which
are closely related are classified together and mixtures of syrups with sim-
ilar compositions are grouped separately from mixtures with different con-
tents. Clearly, ATR-FTIR can detect differences in the fruit syrup content of
juices. Since similar results were found in the previous chapter dealing with
ET technology, it can be concluded that both ATR-FTIR and the ETSPU
have potential for applications in food quality control and food adulteration.
It is, however, not completely possible to group the three multifruit juices
together with the mixtures which have the same syrup composition. Since
the fruit content is the same in both the multifruit juices and the mixtures
related with it, the separation is probably caused by the presence of other
compounds, like the vitamins, aloe vera puree, etc. As showed in the PLS
models based on the analysis of the syrups and fruit juices, ATR-FTIR is,
like the ETSPU, sensitive to small differences in the chemical composition
Fourier transform infrared spectroscopy 147
of a sample. Perfect calibration and validation models were constructed for
all syrups, even those which are only present in very small amounts. The
addition of a syrup to the multifruit juice, furthermore, showed that this is
immediately detected using ATR-FTIR. The addition of extra compounds
like vitamins or aloe vera puree would thus immediately cause large differ-
ences in the absorption spectra of the sample. Detailed information on the
extra compounds could, however, not be given by the manufacturer, due to
the company’s policy on product secrecy. With these results and those found
in literature (Innawong et al., 2004; Lachenmeier, 2007), FTIR seems useful
for quality control and monitoring processes in the food industry because of
its possibility to simultaneously quantify essential compounds and classify
samples. Its speed, good performance and easy use are extra advantages of
FTIR.
4.5 Conclusions
In this chapter, the potential of ATR-FTIR to classify fruit samples and
to quantify their most important taste compounds was studied in several
experiments with a wide variety of samples. ATR-FTIR proved to be a
good tool for recognition, classification, determination and quality control
of fruit juices with different chemical compositions. The short measurement
time and easy use indicate the possibilities of the system as an instrument
in the food industry.
In a first part, the ability of the system to classify samples based on their
taste compounds was studied. Using ATR-FTIR different solutions of glu-
cose, fructose, sucrose, citric acid and malic acid were analyzed to determine
the important peaks in the absorbance spectra. C=O stretching vibrations
and C-O vibrations are related to organic acids and carbohydrates, respec-
tively, which are important taste compounds in fruit. Using the absorbances
at selected wavenumbers, apple and tomato samples were clustered in a sec-
ond experiment. ATR-FTIR proved to be a useful tool for the classification
of cultivars based on their absorbance spectra and, thus, chemical composi-
148 4.5 Conclusions
tion. The influence of sample preparation to classify tomato samples with
ATR-FTIR was studied. Each sample was prepared both as an extract and
a juice and analyzed using a reference technique and ATR-FTIR. Based
on their ATR-FTIR absorbance spectra, the tomatoes were separated less
good as an extract. The absorbance spectra of the extracts showed that a
large amount of chemical information is lost during the extraction process
compared to the juices, which emphasizes the matrix effect. The possibility
to use juices for both reference and ATR-FTIR analysis is a great advan-
tage, which makes the correlation between the reference measurements and
ATR-FTIR analysis more clear and reliable. Less sample preparation also
enhances the possibilities to use ATR-FTIR as a rapid technique, with a
measurement time of only 30 seconds, in high throughput analysis. The
classification results presented are better than those of the ET.
Second, the ability of ATR-FTIR to quantify taste compounds was eval-
uated. Dilution series of individual carbohydrates and organic acids and
mixtures of these compounds show that it is possible to determine their ex-
act concentration in aqueous solutions using ATR-FTIR based on selected
wavenumbers. The use of the minima and maxima of the first derivative
spectra, being the inflection points of the peaks of the absorbance spectra,
gives the most accurate prediction models. The possibility of ATR-FTIR
to determine taste compounds in actual fruit samples was studied using
apple and tomato samples. The predictions, however, were poor. Using
standard additions and dilutions of the samples the concentration ranges
of the taste compounds were enlarged, which resulted in good prediction
models. This indicates that the matrix effects are considerable. Future re-
search should focus on chemometrical methods to separate matrix effects
from useful chemical information.
Finally, the potential of ATR-FTIR as a tool for rapid quality control
was studied. Using IR spectroscopy it is possible to classify the multifruit
juices according to their composition. However, mixtures prepared using
syrups are classified as different from the multifruit juices, possibly due to
the presence of extra compounds in the multifruit juices. Information on
the extra compounds is necessary to make a good classification model based
Fourier transform infrared spectroscopy 149
on the fruit content. The concentration of individual syrups, however, can
be predicted very accurately in the multifruit juices using ATR-FTIR. Us-
ing additions of syrups to the multifruit juices, this technique has proved
to be highly suitable for the detection of small differences between sam-
ples. ATR-FTIR is an important tool for quality control because of its good
performance, easy use and detection speed.
150 4.5 Conclusions
Chapter 5
Sequential injection
ATR-FTIR
5.1 Introduction
In the last decades there has been a shift towards different flow through
systems to speed up and facilitate measurements. Flow injection analysis
(FIA) was first applied by Ruzicka and Hansen in Denmark in 1974 (Ruzicka
and Hansen, 1975). Since then the technique has been further developed and
coupled to spectroscopic and electrochemical detection (Lenehan et al., 2002;
Perez-Olmos et al., 2005). The introduction of sequential injection analysis
(SIA) in the early 1990s broadened the possibilities of flow analysis. SIA
is a technique that has great potential for on-line measurements due to
the simplicity and convenience with which sample manipulations can be
automated (Ruzicka and Marshall, 1990; Economou, 2005).
The synergistic combination between flow through systems and FTIR
developed in the last years provides (i) a simple, fast and reproducible way
for loading and cleaning of the IR flow cells, (ii) repeatability and accuracy,
(iii) an important saving in terms of reagent and time of analysis, (iv) con-
tinuous monitoring of the spectral baseline and accurate determination of
the absorption band maxima, and (v) simultaneous determination of a series
151
152 5.1 Introduction
of compounds in the same sample. The main advantages of IR detection in
flow analysis systems include ease of operation, real-time detection and low
maintenance (Schindler and Lendl, 1999; Gallignani and Brunetto, 2004).
Flow analysis IR techniques are described as powerful analytical techniques
which could provide simple and adequate solutions for the analysis of a lot
of complex and real samples (Lendl and Schindler, 1999). The technique
has been applied to the determination of diverse analytes in fruit juices by
Rosenberg and Kellner (1994) and Kellner et al. (1997).
The potential of ATR-FTIR for food analysis has been demonstrated in
the previous chapter and in many publications. Most described applications,
however, deal with the major disadvantage of extensive sample manipulation
and cleaning of the ATR sampling compartment, which greatly reduces the
sampling rate. To address this problem, in this chapter, the potential of SIA
in combination with ATR-FTIR will be studied for the analysis of Belgian
tomato samples, with a large emphasis on the development and optimization
of the system.
The objective of this chapter is to develop a high throughput technique
using a flow through system based on SIA and FTIR to analyze taste com-
ponents of fruit samples in large scale experiments. Since most of the pub-
lications on flow analysis IR techniques do not deal with the development
and optimization of the measurement technique itself, this will be studied
intensively. Following topics will be studied in this chapter:
� The flow injection system will be optimized for ATR-FTIR measure-
ments of fruit samples. It should be noted that although the system is
specifically optimized for tomato samples, it is applicable for the anal-
ysis of other fruit or vegetable samples containing the same sugars and
acids as tomato.
� The developed SIA-ATR-FTIR technique will be used to analyze real
tomato samples. The potential of the system as an optical tongue to
classify tomato cultivars according to their taste profile will be evalu-
ated and compared to that of the two ET’s which were investigated in
Chapter 3.
Sequential injection ATR-FTIR 153
� The ability of SIA-ATR-FTIR to quantify the organic acid and carbo-
hydrate content of tomato samples will also be evaluated and compared
to the results of an enzyme based reference technique and two ET’s.
The ability of the developed SIA-ATR-FTIR system to predict taste
as scored by a sensory panel will be discussed in Chapter 6.
This chapter is divided in four main sections. In Section 5.2 the materials
and methods which were used are described. The results of experiments with
mixtures and tomatoes are presented in Section 5.3. First, the development
and optimization of the flow through system are described in detail. Second,
the results of the classification of the tomato cultivars are presented. Finally,
calibration models to quantify individual taste compounds are constructed.
In Section 5.4 the results are discussed and compared to literature findings.
Concluding remarks are formulated in Section 5.5. The results presented
in this chapter have been published in Beullens et al. (2006b, 2007c). Two
publications on the optimization of the SIA system and the correlation with
sensory panels have been submitted (Beullens et al., 2008c) and (Beullens
et al., 2008a).
5.2 Materials and Methods
5.2.1 Samples
5.2.1.1 Mixtures
Four mixtures of three sugars (glucose, fructose and sucrose) and four acids
(citric acid, malic acid, quinic acid and tartaric acid) were prepared in tripli-
cate for analysis using the flow analysis ATR-FTIR system. The composition
of the mixtures is shown in Table 5.1a. Stock solutions of the mixtures were
prepared and stored at −80 ◦C until measurement. In an extra experiment
to complete the optimization of the flow injection ATR-FTIR system four
different mixtures of the same sugars and acids were prepared in triplicate.
The composition of the four mixtures is shown in Table 5.1b. The concentra-
154 5.2 Materials and Methods
Table 5.1: Composition of four mixtures used in the optimization of the flow
system coupled to ATR-FTIR. Concentrations are given in g/L.
Glucose Fructose Sucrose Citric Malic Tartaric Quinic
acid acid acid acid
a.
Mixture 1 20 20 5 10 30 10 15
Mixture 2 40 40 20 5 15 15 10
Mixture 3 10 10 30 30 10 5 5
Mixture 4 30 30 10 20 5 2 2
b.
Mixture 1 20 20 5 10 20 5 5
Mixture 2 40 40 20 5 15 10 10
Mixture 3 10 10 30 30 10 2 7
Mixture 4 30 30 10 20 5 7 2
tions of all acids were smaller than in the previous part of the optimization
so that the mixtures resembled more to real fruit samples. Stock solutions
of the mixtures were prepared and also stored at −80 ◦C until measurement.
5.2.1.2 Tomato samples
Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based
on their difference in taste determined by a sensory panel, which is mainly
defined by differences in sweetness and sourness, to assure a broad range
in acid and sugar content. The selected cultivars are: Admiro, Macarena,
Sunstream, Amoroso, Tricia and Clotilde (Table 3.2). The fruit were ob-
tained at the fruit- and vegetable Auctions of Mechelen and Hoogstraten
in Belgium. All tomatoes were picked at ripeness stage 5 (light red class)
(USDA, 1975). The fruit were stored during one day at ambient atmosphere
(18 ◦C and 80% relative humidity). The day after purchase 10 L of tomato
juice was collected in one recipient. Subsequently, the juice was divided over
several falcon tubes and frozen in liquid nitrogen. The samples were stored
at −80 ◦C until analysis with SIA-ATR-FTIR, two types of ET’s (Chapter
3), EHT and a trained sensory panel (Chapter 6).
Sequential injection ATR-FTIR 155
5.2.2 Measurement techniques
5.2.2.1 ATR-FTIR
The ATR-FTIR measurements were performed on a Bruker Tensor 27 spec-
trometer (Bruker, Karlsruhe, Germany) equipped with a mid-IR source and
a MCT detector. The sampling station contained a flow-through horizon-
tal ATR accessory with multiple reflections (PIKE Technologies, Madison,
USA). A closed AMTIR crystal with a channel for sample containment (0.5
mL) was used. Background spectra were collected between every measure-
ment using distilled water. The number of co-added scans and the resolution
were optimized in the flow system. Single beam spectra in the range of 1800
cm−1 to 900 cm−1 were obtained and corrected against the background to
present the spectra in absorbance units. OPUS software version 5.5 (Bruker,
Karlsruhe, Germany) was used to operate the FTIR spectrometer and col-
lect all the data. During ATR-FTIR measurement the samples were placed
in a temperature controlled water bath (Julabo TW8, VWR, Belgium).
5.2.2.2 Sequential injection analyzer
The flow through system consisted of a pump and valve system. The milli-
GAT pump combined with a Microlynx-4 micro-electric controller (Global
FIA Inc., Fox Island, USA) was chosen for its large bi-directional pulseless
flow range from 60 nL/min up to 50 mL/min. A large advantage of the pump
is the redundancy of refill cycles or syringe changes unlike traditionally used
syringe pumps. The stepper motor of the pump is fully computer control-
lable. The milliGAT pump was connected through teflon tubing (1/16”OD
0.030”ID, Alltech Associates Inc., Deerfield, USA) to the ATR cell and a 10-
port injection valve (Cheminert, C22Z-2180D, Valco Instruments Co. Inc.,
Houston, USA) with two valve positions. The valve position, flow rate and
direction were controlled by a computer program written in Labview 8.0 (Na-
tional Instruments Co., Austin, USA). A general schematic flow diagram of
the sequential injection analyzer is shown in Figure 5.1.
156 5.2 Materials and Methods
Valve
Sample
Distilled water
Pump Detector
Waste
Figure 5.1: Schematic flow diagram of the developed sequential injection analyser.
5.2.2.3 Enzymatic high throughput technique
An enzymatic high throughput method, EHT, was used as a reference tech-
nique to evaluate the sugar and acid content of the tomato juices. Details
on the sample preparation and operational settings are given in Chapter 3.
5.2.3 Optimization design
The SIA-ATR-FTIR system was optimized with respect to four parameters:
� resolution
� number of co-added scans
� flow rate
� temperature of the sample.
Sequential injection ATR-FTIR 157
The first three instrumental parameters determine the measurement accu-
racy, the sample volume and the measurement time, while the fourth one
might influence the repeatability of the measurements as influenced by en-
vironmental factors. A central composite design (CCD) was used for the
optimization of the four factors. In a circumscribed CCD the star points
are at a distance α from the center point based on the properties desired
for the design. The star points are extremes for the low and high settings of
all factors (Figure 5.2). This type of design has a hyperspherical symmetry
and requires five levels for each factor (NIST/SEMATECH, 2007).
Facto
r 3
Figure 5.2: Schematic figure of circumscribed central composite design for three
factors.
An overview of the levels per factor is given in Table 5.2. The number of
co-added scans was expressed as a power of two to obtain equidistant levels
for this factor. Compared to a full factorial design, the number of design
points in the CCD was reduced from 625 to 30. For each combination
158 5.2 Materials and Methods
Table 5.2: Four factors to be optimized in the SIA-ATR-FTIR system and the
levels used in the CCD.
Factor Level 1 Level 2 Level 3 Level 4 Level 5
Resolution (cm−1) 1 2 4 8 16
Co-added scans 16 32 64 128 256
Flow rate (µl/sec) 0 15 30 45 60
Temperature (◦C) 10 15 20 25 30
of design factors in the CCD the whole set of mixtures was measured in
triplicate. Next, joint calibration models were calculated for all compounds
in the mixtures using partial least squares regression (PLS2). The root
mean square error of cross-validation (RMSECV) and ratio of prediction
to deviation (RPD) values were used to assess the model performance for
each component individually. Response surfaces of these two values per
compound were constructed as a function of the four factors and their first
order interactions. Optimal factor levels were defined at the minima in the
response surfaces of the RMSECV values and the maxima in the response
surfaces of the RPD values. When the RPD value of a model is equal to
or higher than 2 it can be accepted as a good prediction model. Since
the optimal set of design factors should be representative for all compounds
jointly, also an average RPD value was calculated over all sugars and organic
acids in the mixtures, and used for optimization. The CCD calculations and
the corresponding data analysis were performed in SAS version 9.1 (SAS
Institute Inc., Cary, USA).
5.2.4 Statistical analysis
The SIA-ATR-FTIR data were preprocessed before the statistical analysis
by taking the first derivative of the absorption spectra (Savitsky-Golay algo-
rithm, second order polynomial with 5 points at each side). The wavenum-
bers at which a local minimum or maximum occurred in the derivative spec-
trum were selected. The first derivative absorption spectra at these selected
wavenumbers were used for further data analysis.
Sequential injection ATR-FTIR 159
Multivariate data analysis was applied for quantitative and qualitative
analysis. Partial least squares discriminant analysis (PLS-DA) was per-
formed, as a supervised technique, to cluster the data according to their
chemical composition. The analysis was performed on the covariance ma-
trix. The EHT results were used as a reference to which the SIA-ATR-FTIR
results were compared. Partial least squares regression (PLS2), using cross-
validation, was performed to study the predictive performance of SIA-ATR-
FTIR. The concentration of two sugars, glucose and fructose, and three
acids, citric acid, malic acid and glutamate, were predicted in the tomato
samples using PLS2. The data analysis was carried out using two software
packages: The Unscrambler version 9.0 (CAMO Technologies Inc., Oslo,
Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).
5.3 Results
5.3.1 Optimization
For every design point in the CCD design a PLS2 calibration model was
built. The calibration performance characteristics were calculated for each
of the studied sugars and acids. For each compound an RMSECV response
surface was constructed as a function of the design factors. The optimal set
of operational conditions - the minimum in the response surface - for each
individual compound is depicted in Table 5.3. Based on the levels in the
design, a resolution of 16 cm−1 is optimal for all compounds but malic acid.
256 co-added scans result in a significantly improved prediction performance
for sucrose, citric acid, tartaric acid and quinic acid. A flow of 0 µL/sec
(stopped flow) and a temperature of 10 ◦C is optimal in case of sucrose.
However, the value for the flow and temperature do not significantly affect
the model performance for the other compounds. Although the resolution,
the number of co-added scans and the flow rate all show optimal values
at one of their extreme values, it was decided to use these values together
with a stopped-flow as optimal conditions to minimize RMSECV values in
the PLS2 models. Similar results were found in the optimization of the
160 5.3 Results
RPD values. The maximum RPD values were obtained for a resolution of
16 cm−1, 256 co-added scans, a stopped flow and a sample temperature of
10 ◦C.
Table 5.3: Optimal conditions per compound based on RMSECV results. (NS:
not significant).
Compound Resolution Co-added scans Flow rate Temperature
(cm−1) (µl/sec) (◦C)
Glucose 16 NS NS NS
Fructose 16 NS NS NS
Sucrose 16 256 0 10
Citric acid 16 256 NS NS
Malic acid NS NS NS NS
Tartaric acid 16 256 NS NS
Quinic acid 16 256 NS NS
RPD
RPD
3.60
6
2
6.6
3.6
3012
0
60
4.2
7.8
Figure 5.3: Optimization of resolution, number of scans, flow and temperature
using average RPD values.
Sequential injection ATR-FTIR 161
Since RPD values are dimensionless they are easily averaged out over all
seven compounds, resulting in one RPD value for the whole PLS2 model.
The important design factors were a resolution of 16 cm−1, 256 co-added
scans and a temperature of 25 ◦C (Figure 5.3). For the factor temperature,
these results slightly differ from the previous analysis using RMSECV and
individual RPD values. Hence, an extra experiment was performed to find
the optimal level of temperature.
5
6
7
8
9
10
PD
0
1
2
3
4
5
10 20 30 40 50
RP
Temperature (°C)
Figure 5.4: Optimization of the temperature using average RPD values and op-
timal settings for resolution, number of co-added scans and flow rate.
In this second experiment the resolution, number of co-added scans and
flow rate were taken as optimal, respectively 16 cm−1, 256 co-added scans
and 0 µl/sec. PLS2 calibration curves were made for four mixtures of sugars
and acids (Table 5.1b) at five different temperatures: 10 ◦C, 20 ◦C, 30 ◦C,
40 ◦C and 50 ◦C. The overall RPD value was used to find the optimal tem-
perature. No significant differences were found between the five RPD values
162 5.3 Results
(Figure 5.4). Temperature plays no significant role in the flow injection
ATR-FTIR system to analyze sugars and organic acids. It was chosen to
carry out all measurements at room temperature (± 20 ◦C).
5.3.2 Data exploration of tomato samples
0.00
0.02
0.04
0.06
0.08
0.10
0.12
1790
1743
1697
1651
1605
1558
1512
1466
1419
1373
1327
1281
1234
1188
1142
1095
1049
1003 957
910
Abs
orba
nce
Wavenumber (cm-1)
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
Figure 5.5: Average ATR-FTIR absorbance spectra of six tomato cultivars.
Figure 5.5 and 5.6 respectively show the absorbance spectrum and first
derivative of the absorbance spectrum of the six tomato cultivars. Large
differences between the tomato cultivars are visible in the absorbance spec-
tra. Amoroso displays a higher absorbance at almost all wavenumbers, es-
pecially in the spectral region between 1140 cm−1 and 1000 cm−1. At these
wavenumbers absorption of light occurs due to strong stretching vibrations
of the C-O bonds present in sugars. Amoroso is known for its sweet and sour
taste; it is a very tasty fruit. The results of the reference measurements (Ta-
Sequential injection ATR-FTIR 163
ble 3.2) showed that Amoroso contains high concentrations of two sugars,
glucose and fructose. Admiro, however, absorbs more light than Amoroso
and the four other cultivars between 1720 cm−1 and 1620 cm−1. This area
in the spectrum is related to the absorbance light by the C=O stretching
bonds of organic acids. This corresponds to the fact that Amoroso contains
a low concentration of malic acid. Tricia absorbs less IR light than the
other five cultivars at all wavenumbers. Table 3.2 showed that this cultivar
does not contain high concentrations of any of the studied sugars and acids.
Tricia is known as a tomato with a weak taste. The information on the
concentrations of taste compounds in the six cultivars as measured using a
reference technique can thus be reflected in the amount of absorbed IR light.
-0.010
-0.008
-0.006
-0.004
-0.002
0.000
0.002
0.004
0.006
0.008
0.010
1790
1743
1697
1651
1605
1558
1512
1466
1419
1373
1327
1281
1234
1188
1142
1095
1049
1003 957
910
Firs
t der
ivat
ive
abso
rban
ce
Wavenumber (cm-1)
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
Figure 5.6: Average first derivative of ATR-FTIR absorbance spectra of six
tomato cultivars.
Based on the first derivative absorbance spectra following wavenumbers
were selected for further data analysis: 1751 cm−1, 1689 cm−1, 1612 cm−1,
164 5.3 Results
1542 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1, 1326 cm−1, 1265 cm−1,
1218 cm−1, 1103 cm−1 and 1018 cm−1. Despite the different settings of the
resolution and number of scans, most of these wavenumbers are close to the
ones selected in previous experiments with ATR-FTIR (Table 4.9).
5.3.3 Classification of tomato cultivars
The results of the PLS-DA performed on the data of the reference measure-
ments are shown in Figure 5.7. Separation between cultivars based on their
sugar and acid content is clearly achieved using EHT. Almost all separa-
tion occurs along the axis of the first PC. The variation explained by the
second PC is due to the technique used in the analysis. Amoroso, which is
separated from the other cultivars, is highly positively correlated with glu-
tamate, glucose and fructose. Macarena, Sunstream, Tricia and Clotilde are
not correlated to any of the sugars or acids which were measured. Admiro
shows a correlation with malic acid.
Figure 5.8 shows the results of the PLS-DA performed on the absorbances
at the 20 selected wavenumbers of the SIA-ATR-FTIR measurements. The
classification along the axis of the first PC is related to the sugar content
of the samples. Amoroso, which is classified at the negative end of the axis,
contains the highest concentrations of sugars. Tricia is classified at the pos-
itive end of the axis. This cultivar contains the lowest concentrations of
glucose and fructose. Sunstream, Macarena and Clotilde all have a sugar
content which is in between that of Amoroso and Tricia. The first PC can
thus be seen as a sugar axis. 1612 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1,
1326 cm−1, 1218 cm−1, 1103 cm−1 and 1018 cm−1 show a correlation of al-
most 100% with the first PC. These wavenumbers are part of the spectral
area in which absorption of IR light is mainly caused by C-O vibrations
in sugars. Admiro is separated from the other cultivars along the axis of
the second PC. The classification table (Table 5.4) also shows that all of
the samples are classified within the correct cultivar using SIA-ATR-FTIR.
The findings of the PLS-DA are in accordance with those from the reference
technique, EHT.
Sequential injection ATR-FTIR 165
-2
-1
0
1
2
-14 -12 -10 -8 -6 -4 -2 0 2 4 6 8
PC
2
PC 1
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
A
Citric acid
Malic acid
Admiro
Macarena
Sunstream0.2
0.4
0.6
0.8
1.0
PC
2
PC 1GlucoseFructose
GlutamateAmoroso
Tricia
Clotilde
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC 1
B
Figure 5.7: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
tomato samples measured by EHT (X-expl. (98%, 1%); Y-expl. (20%, 16%)).
166 5.3 Results
0 0002
0.0004
0.0006
0.0008
PC
2AdmiroMacarenaSunstreamAmorosoTriciaClotilde
-0.0004
-0.0002
0.0000
0.0002
-0.006 -0.004 -0.002 0.000 0.002 0.004 0.006
PC 1
A
1751
16121450
1373
1265
12181018
Admiro
Sunstream
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
1689
15421349
132612181103
MacarenaAmoroso
Tricia
Clotilde
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
B
Figure 5.8: Score plot (A) and correlation loadings plot (B) of the PLS-DA of the
tomato sample measured by SIA-ATR-FTIR (X-expl. (98%, 1%); Y-expl. (19%,
16%)).
Sequential injection ATR-FTIR 167
Table 5.4: Classification results of the PLS-DA performed on the reference data
and SIA-ATR-FTIR measurements. The percentage of correct classified samples is
shown (%).
Cultivar EHT SIA-ATR-FTIR
Admiro 80 100
Macarena 100 100
Sunstream 100 100
Amoroso 100 100
Tricia 100 100
Clotilde 100 100
5.3.4 Quantification of taste compounds
A PLS2 analysis was performed to predict the sugar and acid concentration
in the six tomato cultivars based on their SIA-ATR-FTIR absorption spec-
tra. The results of the analysis are shown in Table 5.5. Four PC’s were used
to find these results. The correlations between the measured and predicted
concentrations of glucose and fructose are high for both the calibration and
the validation models. The offsets of the validation models and RMSECV
values are relatively high, however, high RPD values of 5.0 and 4.1 can
be found for respectively glucose and fructose. The prediction of the acid
concentrations shows different results. The correlations of both calibration
and validation models of citric acid and glutamate are a bit lower, but the
RMSECV values are sufficiently low and the RPD values are higher than 2,
with values of respectively 2.2 and 2.5. The correlation between the mea-
sured and predicted malic acid concentration reaches a value of 0.76. The
RMSECV values of malic acid are low and the RPD value of the prediction
model is 1.6. The difference in correlation between measured and predicted
concentrations of fructose and glucose, the two sugars, on one hand and
citric acid and glutamate on the other hand are notable. This might be
explained by the concentration range of the individual compounds which is
present in the samples. Glucose and fructose respectively have a range from
10 g/L to 24 g/L and 11 g/L to 24 g/L (Table 3.12). The concentration
ranges of citric acid and glutamate are respectively 2.5 g/L to 6.1 g/L and
168 5.4 Discussion
0.6 g/L to 2.9 g/L. Despite these differences, all four prediction models have
an RPD value higher than 2, which indicates that all models are accurate
to predict that specific component. The correlation between the measured
and predicted malic acid concentration reaches a values of 0.76 in the val-
idation model. The range of concentration of malic acid in the samples is
very small, from 0.3 g/L to 0.9 g/L. With this very small range in tomato
it is not possible to predict the concentration of malic acid accurately using
SIA-ATR-FTIR.
Table 5.5: PLS2 models based on the SIA-ATR-FTIR measurements of the tomato
samples. Cross-validation was used to validate the model. The offsets, RMSEC and
RMSECV values are given in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Glucose Calibration 0.99 0.23 0.99 0.53
Validation 0.94 1.06 0.98 0.93 5.0
Fructose Calibration 0.98 0.36 0.99 0.62
Validation 0.91 1.46 0.96 1.09 4.1
Citric acid Calibration 0.89 0.51 0.94 0.38
Validation 0.83 0.73 0.90 0.49 2.2
Malic acid Calibration 0.81 0.11 0.90 0.08
Validation 0.60 0.22 0.76 0.12 1.6
Glutamate Calibration 0.92 0.10 0.96 0.19
Validation 0.81 0.25 0.90 0.29 2.5
5.4 Discussion
5.4.1 Optimization
A SIA-ATR-FTIR system was developed and optimized for the analysis
of fruit samples using mixtures of their taste compounds. The optimal
settings, found through studying the average RPD value of the prediction
models of seven taste compounds, are a resolution of 16 cm−1, 256 co-
added scans and a stopped-flow. Since a stopped-flow is used, the sample
Sequential injection ATR-FTIR 169
volume is minimized and the measurements can be compared to those of
the static system. However, a different resolution and number of co-added
scans is preferred in this SIA system. This indicates that the measurements
performed and discussed in Chapter 4 may give better results when using
a resolution of 16 cm−1 and 256 co-added scans instead of a resolution of 4
cm−1 and 128 scans. It must be stressed that a resolution of 16 cm−1 could
cause a loss in chemical information and therefor is not preferred in FTIR
spectroscopy. The results of the optimization, however, indicate an optimal
quantification of the compounds of interest at this resolution. Long-term
response instability of FTIR spectrometers have been observed in many
studies (Griffiths and de Haseth, 2007). As sample temperature changes,
the density changes and corresponding changes in refractive index occur. In
addition, the width of the absorption bands increases with temperature and
because their area is relatively constant, peak height decreases (Chalmers
and Griffiths, 2002). In this thesis, however, temperature was found not
to have a significant influence on the measurements. The measurement of a
background spectrum immediately before each sample and the measurement
time of less than one minute per sample, make the influence of temperature
insignificant. MacBride et al. (1997) concluded from their experiments that
changes in room temperature should still be minimized, since they can affect
spectrometer stability directly.
5.4.2 Classification of tomato cultivars
The ability of the system to classify tomato cultivars based on their taste
profile was studied. Using the SIA-ATR-FTIR system, tomato cultivars
can be discriminated based on their absorbance spectra and, thus, chemical
composition. The absorbance spectra registered in this experiment show
less noise than the ones shown in Chapter 4. This is the result of the
differences in resolution and number of co-added scans between the dynamic
and static system, with values that are respectively 16 cm−1 and 4 cm−1
and 256 co-added scans and 128 co-added scans. The main vibrations on
which the classification is based are again situated in the regions with C-
170 5.4 Discussion
O stretching vibrations of carbohydrates and C=O vibrations of organic
acids. SIA-ATR-FTIR is a good technique to classify samples based on
their taste compounds. Comparing the technique to ET technology, shows
that both the ETSPU and the ASTREE ET cannot discriminate between
the samples as clear as SIA-ATR-FTIR does. Both ET’s work as black
box systems, making it more difficult to relate the classification directly to
chemical compounds. A direct correlation is possible in ATR-FTIR and
SIA-ATR-FTIR due to the specific vibrations of chemical compounds.
5.4.3 Quantification of taste compounds
The good classification based on the taste compounds was also reflected in
the possibility of the system to quantify the individual compounds. The
prediction models of glucose and fructose are very accurate, indicating that
both compounds can be predicted in tomato juice using SIA-ATR-FTIR.
The reason for this is the high content of sugars in the samples. Schindler
et al. (1998b) stressed the importance of the characteristic absorbance spec-
tra of sugars to identify them individually in complex samples. The models
predicting citric acid and glutamate show a possibility of the flow through
system coupled to ATR-FTIR to determine the content of both acids in fruit
samples. Malic acid cannot be predicted accurately due to small concentra-
tion ranges in tomato. This same problem was encountered by Schindler
et al. (1998a) while making prediction models for taste compounds in wine.
The models for tartaric acid and acetic acid, which are present in small
concentration ranges, showed low correlations between the measured and
predicted values in model solutions of the taste compounds. Using standard
additions of malic acid or fruit with a higher content and larger differences
in malic acid, the ability of the system to predict this compound could be
studied in future experiments. SIA-ATR-FTIR has proved to be a better
technique to determine the composition of fruit than both ET’s. This again
can be related to the very specific absorbance spectra of the individual com-
pounds and the cross-sensitivity of the sensors in the ET’s making that one
single sensor cannot predict the content of the individual taste compounds
Sequential injection ATR-FTIR 171
(Legin et al., 1997). The results found in this experiment are better than in
the previous chapter. As mentioned before, this is due to the differences in
settings of the instrument and the wider range of sugars and acids present
in the samples used in the experiment reported in this chapter.
5.5 Conclusions
The possibility of SIA-ATR-FTIR to classify food samples and quantify their
taste compounds was studied in this chapter.
A flow through system was developed and optimized to enable and fa-
cilitate high throughput measurements of tomato using ATR-FTIR. In an
experiment based on a CCD the optimal settings were found to maximize
the RPD values of the calibration curves built for the most abundant sugars
and acids in tomato. A resolution of 16 cm−1, 256 co-added scans and a
stopped flow are optimal settings. Temperature is not significant, which
allows working at room temperature.
The potential of this SIA-ATR-FTIR system as a high throughput tool
was studied in an experiment with six tomato cultivars chosen for their
difference in taste. The developed technique makes it possible to discrimi-
nate between tomato cultivars based on their specific absorption of sugars
and acids and, thus, their chemical content. Glucose and fructose are com-
pounds on which classification models are built easily due to their specific
absorbance bands. The system is able to predict the concentration of the
sugars glucose and fructose as measured using a reference technique based
on enzymatic reactions. It is also possible to predict the citric acid and
glutamate content in tomato, however correlations between measured and
predicted values are lower. The concentration of malic acid cannot be pre-
dicted due to the small concentration range present in the studied fruit. The
advantages of SIA-ATR-FTIR were demonstrated in this experiment: fast
analysis, high sample throughput, low operational costs and versatility and
simplicity of the flow injection instrumentation.
172 5.5 Conclusions
Future research involving SIA-ATR-FTIR could involve the introduc-
tion of enzymes in the system. Using these enzymes, chemical compounds
are identified individually based on their response to the enzymes, making
it possible to determine compounds in low concentrations and compounds
with absorbance spectra which are similar to each other. This enzyme based
methodology was first introduced by Lendl and Kellner (1995) for the anal-
ysis of sugars and Le Thanh and Lendl (2000) for the combined analysis of
sugars and organic acids in food samples. A next step in the development of
a flow through ATR-FTIR system for high throughput taste analysis would
be to minimize the system towards a micro-total analytical system (µ-TAS).
Chapter 6
Relation between sensory
analysis and instrumental
measurements
6.1 Introduction
Traditionally, both sensory and instrumental techniques are used to deter-
mine the taste of horticultural commodities. Although sensory panel anal-
ysis is by far the most realistic technique to obtain information on human
taste perception, it has some problems including the standardization of mea-
surements and reproducibility. Two other drawbacks of this technique are
the high cost and taste saturation of the panelist (Meilgaard et al., 2007).
Because of these drawbacks, there is a need to relate rapid, low cost and
simple methods of analysis and quality assessment to sensory panel stud-
ies in food industry. Many publications have showed the potential of ET
technology and ATR-FTIR in the determination of chemical components.
The correlation between both rapid techniques and taste as preceived by a
sensory panels or consumers, however, is less studied. Literature shows the
relation between the ET and sensory analysis in rice with different milling
yields (Tran et al., 2004), Italian wine from different vineyards (Legin et al.,
173
174 6.1 Introduction
2003) and nine commercial apple juices (Bleibaum et al., 2002). FTIR has
been coupled to sensory analysis in only one study: the classification of
Austrian pumpkin seed oil brands (Lankmayr et al., 2004).
In the previous chapters of this work, the possibilities of two ET’s and
SIA-ATR-FTIR as tools for clasification of fruit cultivars and the quan-
tification of their most important taste components were indicated. The
objective of this chapter is to study the potential of these rapid instrumen-
tal techniques to predict taste as determined by a sensory panel. This will
take place in two stages.
� The potential of the trained sensory panel to measure taste and to
respond to small differences in the chemical composition of a sample
will be evaluated. Taste compounds will be added in different con-
centrations to a weak tasting tomato juice to determine the effect of
individual taste compounds on the different sensory taste attributes.
� The potential of the ETSPU, the ASTREE ET and SIA-ATR-FTIR
to predict the different sensory taste attributes of six tomato cultivars
will be studied. The results of a sensory panel analysis will be related
to those of the instrumental techniques using preference mapping. It
reflects how well the information of the sensory analysis is described
in the results of the instrumental measurements.
This chapter is divided in four main sections. In Section 6.2 the materials
and methods are described. The section discusses the samples, instrumental
techniques and sensory analysis applied in the two experiments. The results
of both experiments are given in Section 6.3. In Section 6.4 the results are
discussed and compared to the instrumental measurements and literature
findings. Concluding remarks are formulated in Section 6.5. The results of
the second experiment presented in this chapter are described in Beullens
et al. (2008b,c,a).
Relation between sensory analysis and instrumental measurements 175
6.2 Materials and methods
6.2.1 Samples
6.2.1.1 Addition of chemical taste compounds to a tomato juice
A tomato cultivar with a weak taste, Tricia, was chosen for this experiment.
Twenty fruit of this cultivar were obtained at the fruit- and vegetable Auc-
tions of Mechelen and Hoogstraten in Belgium. All tomatoes were picked
at ripeness stage 5 (light red class) (USDA, 1975) and stored at ambient
atmosphere (18 ◦C and 80% relative humidity). One week after purchase
the fruit were made into a juice and collected in a large recipient. Subse-
quently, the juice was divided over several falcon tubes of 200 mL, frozen
in liquid nitrogen and stored at −80 ◦C until the day of analysis. Before
analysis the samples were defrosted and sugars, acids, NaCl and taste en-
hancer were added to the juice. A central composite design (CCD) was used
to minimize the amount of measurements needed in the experiment. Four
factors were studied in the design at 3 levels. The four factors are sweetness
(fructose), sourness (citric acid), saltiness (NaCl, kitchensalt) and umami
(Monosodium glutamate, Ve-Tsin taste enhancer from Chinese restaurant).
Three levels are used in this experiment: (i) no addition, (ii) addition to
an average tomato and (iii) addition to an extreme tasting tomato. The
composition of the average and extreme tasting tomato were considered as
found in previous experiments (Table 3.12). The added concentrations were
determined based on knowledge of the composition of tomato cultivars found
in previous experiments. The total CCD consists of 31 measurements (Ta-
ble 6.1). The samples were analyzed using a trained sensory panel at the
Sensory Laboratory at the Vegetable Research Centre in Kruishoutem, Bel-
gium. The sugar and acid content of the Tricia tomato sample was analyzed
using an enzyme based reference technique (EHT).
176 6.2 Materials and methods
Table 6.1: Addition of pure compounds to tomato juice in a Central Composite
Design (g/L).
Factor Fructose Citric acid NaCl Taste enhancer
1 0 0 0 0
2 0 0 0 2
3 0 0 0.07 0
4 0 0 0.07 2
5 0 3 0 0
6 0 3 0 2
7 0 3 0.07 0
8 0 3 0.07 2
9 13 0 0 0
10 13 0 0 2
11 13 0 0.07 0
12 13 0 0.07 2
13 13 3 0 0
14 13 3 0 2
15 13 3 0.07 0
16 13 3 0.07 2
17 0 1.5 0.04 1
18 13 1.5 0.04 1
19 6.5 0 0.04 1
20 6.5 3 0.04 1
21 6.5 1.5 0 1
22 6.5 1.5 0.07 1
23 6.5 1.5 0.04 0
24 6.5 1.5 0.04 2
25 6.5 1.5 0.04 1
26 6.5 1.5 0.04 1
27 6.5 1.5 0.04 1
28 6.5 1.5 0.04 1
29 6.5 1.5 0.04 1
30 6.5 1.5 0.04 1
31 6.5 1.5 0.04 1
Relation between sensory analysis and instrumental measurements 177
6.2.1.2 Tomatoes with a wide range of tastes
Six tomato cultivars (Lycopersicon esculentum Mill.) were selected based
on their difference in taste determined by a sensory panel (Buysens, 2006a).
Since the taste of tomatoes is mainly defined by differences in sweetness and
sourness, a broad range in acid and sugar content was assured. The selected
cultivars were: Admiro, Macarena, Sunstream, Amoroso, Tricia and Clotilde
(Table 3.2). The fruit were obtained at the fruit- and vegetable Auctions of
Mechelen and Hoogstraten in Belgium. All tomatoes were picked at ripeness
stage 5 (light red class) (USDA, 1975). The fruit were stored during one day
at ambient atmosphere (18 ◦C and 80% relative humidity). The day after
purchase 10 L of tomato juice per cultivar was collected in one recipient.
Subsequently, the juice was divided over falcon tubes for instrumental and
sensory panel analysis and frozen in liquid nitrogen. The samples were stored
at −80 ◦C until measurement using SIA-ATR-FTIR (Chapter 5), two types
of ET’s (Chapter 3), two reference techniques (EHT and AAS) and a trained
sensory panel. Using reference techniques, the concentration of two sugars,
glucose and fructose, three acids, citric acid, malic acid and glutamate, and
two minerals, Na and K, in the tomato samples was determined. The frozen
tomato samples for sensory analysis were send to the Sensory Laboratory at
the Vegetable Research Centre in Kruishoutem, Belgium.
6.2.2 Sensory panel analysis
The sensory panel analysis of the samples was conducted in the Sensory Lab-
oratory at the Vegetable Research Centre in Kruishoutem, Belgium. The
sensory laboratory houses a test room with 14 individual booths constructed
according to the ISO 8589 norm (ISO, 1988). A panel of thirteen panelists
was trained over a 6-week period to evaluate sensory attributes of tomatoes
focusing on taste. A list of the sensory attributes and reference compounds
used for training is given in Table 6.2. The reference compounds were chosen
based on their importance in tomato. Only nine out of the thirteen panelists
were chosen for the experiments based on their participation and presence
178 6.2 Materials and methods
Table 6.2: Sensory attributes and references used to score tomato taste.
Attribute Reference
Sweetness Fructose
Sourness Citric acid
Saltiness NaCl
Umami Monosodium-glutamate (taste enhancer from Delhaize)
during the sessions. Since the instrumental measurements described in this
thesis were performed on tomato juices, the panelists were also trained using
juices. Previous experiments at the Sensory Laboratory, however, indicated
that high correlations exist between whole tomatoes and their juices when
scored on their taste attributes by a trained panel (Buysens, 2006b). The
tomato samples of both experiments were evaluated on their sweetness, sour-
ness, saltiness and umami taste in each session. The panelists were asked to
score the taste attributes of the tomato juice contained in closed cups. The
evaluations were performed at room temperature (18−20 ◦C) under red light
to exclude the effect of color. Samples were presented in a comparative way
using a Latin square design to avoid effects of order and first position. For
each product, the assessors scored intensities for the perceived attributes on
unstructured 10 cm line scales anchored by the terms ’weak’ (0) and ’strong’
(10). Between samples panelists could rinse their mouth with water and eat
white bread without added salt. Six samples were analyzed per session.
6.2.3 Instrumental techniques
An enzymatic high throughput method (EHT) and AAS were used as ref-
erence techniques to evaluate the sugar, acid and mineral content of the
tomato juices. Details on the sample preparation and operational settings
are given in Chapter 3.
The six tomato cultivars were analyzed with two types of ET’s and SIA-
ATR-FTIR. Detailed information on the sample preparation and measure-
ment protocol is given in Chapter 3 and Chapter 5.
Relation between sensory analysis and instrumental measurements 179
6.2.4 Statistical analysis
The data of the experiment dealing with the addition of chemical com-
pounds to a tomato juice were analyzed into detail in three steps. In a first
analysis the correlations between the four studied sensory taste attributes
were explored in panel scores correlation matrices. Second, the main effects,
interactions and lack-of-fit were studied for each attribute using response
surface regression models (SAS, 2007). And finally, using PLS2 models the
predictive capacity of the sensory panel is quantified.
Multivariate data analysis was applied for both qualitative and quanti-
tative analysis of the taste attributes of the six tomato cultivars as measured
by the sensory panel. Partial least squares-discriminant analysis (PLS-DA)
was used for data visualization and clustering of observations in the data
structure. Using this technique, intra-cultivar effects are minimized and
inter-cultivar effects are maximized. The analysis was performed on the
covariance matrix. Outliers were deleted from the analysis based on their
scores, leverages (distance to the model centre for each object summarized
over all components) and residuals (Geladi and Dabakk, 1995). The re-
sults of the PLS-DA performed on the sensory panel data were compared
to those of the reference techniques and the rapid instrumental techniques.
Preference mapping, using partial least squares analysis (PLS), was used
to correlate instrumental techniques to sensory panel analysis. The predic-
tive capacity of the instrumental techniques for sensory taste attributes was
studied using PLS2 cross-validation models (Martens and Naes, 1998; Naes
et al., 2004). For all data analysis two different computer software programs
were used: The Unscrambler version 9.1.2 (CAMO Technologies Inc., Oslo,
Norway) and SAS version 9.1 (SAS Institute Inc., Cary, USA).
180 6.3 Results
6.3 Results
6.3.1 Addition of chemical taste compounds to a tomato
juice
The correlations between the taste attributes as scored by the panelists
after addition of chemical compounds to a juice of a neutral tasting tomato
are listed in Table 6.3. The correlation matrix shows that there are small
correlations between the taste attributes. These correlations, however, are
not significant according to the probability values.
Table 6.3: Correlation matrix of four taste attributes as scored by a sensory panel
after addition of taste compounds to a tomato juice.
Taste Sweetness Sourness Saltiness Umami
Sweetness 1.0
Sourness 0.32 1.0
Saltiness 0.27 -0.12 1.0
Umami -0.30 0.070 -0.14 1.00
The main effects and interactions of the chemical compounds in the
samples to each taste attribute were studied in a response surface regression
model. The results of this analysis learn that sweetness is mainly affected
positively by the fructose and citric acid composition and to a lesser extent
by the glutamate content of the samples. The fructose content influences the
sweetness in a quadratic response with a lack-of-fit. No cross products be-
tween chemical compounds were found significant for sweet taste perception.
Sourness is positively affected by citric acid. An interaction between citric
acid and fructose exists. This indicates that fructose causes a sourness taste
suppression. A lack-of-fit was found in the analysis. Saltiness is positively
affected by the content of citric acid and the quadratic effect of NaCl. None
of the taste compounds are significant to the perception of umami taste,
although NaCl has some influence. These unexpected results for umami
could be explained by a discrepancy in the training of the panelists. The
sensory panel was trained on tasting umami using a reference compound
Relation between sensory analysis and instrumental measurements 181
called ’taste enhancer’ from Delhaize. This taste enhancer is composed out
of 97% monosodium glutamate and some additives, mainly salts. During the
experiment, however, an other taste enhancer was used since the Delhaize
brand was not available anymore. The taste enhancer used as an addition to
the tomato juice in the experiment is called Ve-Tsin and was purchased at
a Chinese restaurant. Ve-Tsin is composed of only 92% monosodium gluta-
mate and 8% salt. The addition of this taste enhancer thus has an influence
both on the results found for saltiness and umami. Both for saltiness and
umami a lack-of-fit was found.
Table 6.4: PLS2 calibration models to predict sensory taste attributes built on
the known information on addition of chemical compounds. Validation models were
made using cross-validation. The offsets, RMSEC and RMSECV values are given
in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Sweetness Calibration 0.89 0.54 0.94 0.57
Validation 0.87 0.61 0.93 0.63 2.7
Sourness Calibration 0.87 0.54 0.93 0.56
Validation 0.82 0.74 0.91 0.64 2.5
Saltiness Calibration 0.57 1.63 0.75 0.42
Validation 0.51 1.87 0.68 0.48 1.6
Umami Calibration 0.07 3.77 0.26 0.75
Validation -0.05 4.28 -0.19 0.85 0.9
PLS2 cross-validation models were built to find correlations between the
added taste compounds and the scored sensory taste attributes. Sweetness
and sourness can be determined very well using sensory panels in a PLS2
analysis using two PC’s. The results of the analysis are shown in Table
6.4. After training the panelists on the quantification of fructose and citric
acid as references for sweetness and sourness, a very good prediction of
both sensory taste attributes was observed. The RMSE values are low,
resulting in relatively high RPD values. The RPD values of the models
made for sweetness and sourness are respectively 2.7 and 2.5. The amount
of fructose and citric acid added to the tomato juices covered a wide range,
182 6.3 Results
comparable to what is found in the Belgian tomato cultivars. The prediction
models of saltiness and umami are less good. This could be explained by the
discrepancy in the training of the sensory panel with the reference compound
which was described earlier. The high offsets found in all models are a result
of the chemical composition of the tomatoes to which chemical compounds
were added. The composition of this tomato was not taken into account in
this analysis.
6.3.2 Tomatoes with a wide range of tastes
According to the panelists, Amoroso is the most sweet tasting tomato and
has a strong umami taste (Tabel 6.5). Together with Tricia and Clotilde, this
cultivar, is scored as not sour. Admiro is more sour than the other cultivars.
Admiro, Tricia and Clotilde get low scores for umami. The differences in
saltiness are smaller than for the other tastes.
Table 6.5: Average results of panel scores of the tomato samples (average ±standard deviation, scores between 0 and 10).
Cultivar Sweetness Sourness Umami Saltiness
Admiro 2.2±0.3 5.6±0.2 2.9±0.2 3.7±0.5
Macarena 3.3±0.5 4.0±0.5 3.3±0.6 3.8±0.5
Sunstream 4.7±0.3 4.4±0.3 4.3±0.7 4.1±0.5
Amoroso 6.8±0.7 3.1±0.3 4.6±0.1 3.4±0.5
Tricia 2.3±0.3 3.3±0.4 2.3±0.8 2.6±0.5
Clotilde 3.6±0.7 3.4±0.2 2.7±0.2 2.6±0.2
A PLS-DA was performed on the panel scores of the six tomato cultivars.
The results are shown in Figure 6.1. The score plot shows that Amoroso is
separated from the other cultivars along the axis of the first PC, which is
highly correlated with sweetness. This cultivar was also scored as a sweet
tomato by the sensory panel (Table 6.5). The results of the reference analysis
indicated that Amoroso contains high concentrations of glucose and fructose
and a low concentration of malic acid and that Amoroso is highly correlated
with glutamate, glucose and fructose.
Relation between sensory analysis and instrumental measurements 183
-2
-1
0
1
2
-5 -4 -3 -2 -1 0 1 2 3
PC
2
PC 1
AdmiroMacarenaSunstreamAmorosoTriciaClotilde
A
Sweetness
Amoroso
Clotilde Tricia
0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
SournessSaltiness
Umami
Admiro
Macarena
Sunstream
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
B
Figure 6.1: Score plot and correlation loadings of the PLS-DA based on the
sensory panel scores (X-expl. (70%, 22%); Y-expl. (20%, 19%)).
184 6.3 Results
Malic acidCitric acid
K
SOURNESSSALTINESS
UMAMI0.2
0.4
0.6
0.8
1.0
PC
2
PC 1Glucose
Fructose
Glutamate
Na
K
SWEETNESS
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC 1
Figure 6.2: Correlation loadings of the first two PC’s based on the sensory panel
scores and measurements of the reference techniques EHT and AAS (X-expl. (99%,
1%); Y-expl. (66%, 19%)).
The previous experiment and the correlation loadings between the panel
scores and the reference measurements (Figure 6.2) show that sweetness is
highly correlated with the glucose and fructose content. Admiro is posi-
tioned in the second quadrant of the score plot and is also separated from
the other cultivars. This cultivar is highly correlated with sourness. This
sourness, however, can not be explained directly by the chemical composi-
tion of this cultivar. Admiro does not contain high concentrations of citric
acid and malic acid. Figure 6.2 shows that sourness is not correlated with
citric acid, the most abundant acid in tomato. The samples of both Admiro
and Amoroso are all classified correctly, which is in contrast to the other
cultivars. Clotilde and Tricia are separated from the other cultivars but not
from each other. Only 25% of the samples of Tricia are classified correctly
by the sensory panel. Sunstream and Macarena are also separated from the
Relation between sensory analysis and instrumental measurements 185
other cultivars. None of these four cultivars is highly correlated with any of
the sensory taste attributes in the plane of the first two PC’s. The sensory
panel is not able to classify the samples belonging to Clotilde, Sunstream
and Macarena correctly.
The calibration and validation models of the panel scores of the four sen-
sory taste attributes as predicted by the reference measurements are shown
in Table 6.6. Three PC’s were used to build these models. All models show
high slopes, low offsets and high correlations between the predicted and
measured values. The RMSEC and RMSECV values are low, resulting in
high RPD values for all models. The RPD of sweetness, sourness, saltiness
and umami are high with values of respectively 3.4, 4.6, 3.3 and 3.6. The
correlation loadings plot (Figure 6.2) shows that sweetness is highly corre-
lated with glucose and fructose as measured using the EHT method. Some
correlation also occurs between umami and glutamate and saltiness and Na.
Sourness is correlated with malic acid and not citric acid, which is surprising
since the panelists were trained on tasting sourness using citric acid. The
large differences in the human detection threshold of sourness between malic
acid and citric acid might be an explanation for this result (Stahl, 1973).
Table 6.6: PLS2 calibration models to predict taste built on the results of the
measurements of the the enzymatic reference technique and AAS. Validation models
are built using cross-validation. The offsets, RMSEC and RMSECV values are given
in g/L.
Compound Slope Offset Correlation RMSEC RPD
RMSECV
Sweetness Calibration 0.91 0.33 0.96 0.49
Validation 0.89 0.40 0.93 0.62 3.4
Sourness Calibration 0.86 0.55 0.93 0.34
Validation 0.83 0.66 0.90 0.41 4.6
Saltiness Calibration 0.63 1.22 0.80 0.42
Validation 0.56 1.50 0.69 0.51 3.3
Umami Calibration 0.79 0.72 0.89 0.46
Validation 0.76 0.82 0.84 0.52 3.6
186 6.3 Results
Table 6.7: Correlations (R), RMSECV and RPD values found in the PLS2 models
to predict taste as scored by a sensory panel using the ETSPU, ASTREE ET
and SIA-ATR-FTIR. Cross validation was used to build validation models. The
RMSECV values are given as scores between 0 and 10.
Taste ETSPU ASTREE ET SIA-ATR-FTIR
Sweetness R 0.48 0.80 0.94
RMSECV 1.47 1.22 0.58
RPD 1.4 1.7 3.6
Sourness R 0.76 0.70 0.91
RMSECV 0.64 0.69 0.39
RPD 2.9 2.7 4.8
Saltiness R 0.84 0.94 0.87
RMSECV 0.42 0.41 0.50
RPD 4.4 4.5 3.7
Umami R 0.57 0.85 0.77
RMSECV 0.80 0.69 0.47
RPD 2.4 2.4 3.5
The potential of both the ETSPU and the ASTREE ET to predict sen-
sory panel scores is studied in a PLS2 analysis. The main results of the
PLS2 validation models of both multisensor systems using four PC’s are
shown in Table 6.7. The ETSPU gives very good results for all calibration
models (results not shown). All slopes and correlations are close to 1 and
the offsets and RMSEC values are low. Low values for the slopes and high
values for the offset are found in all cross-validation models. The correla-
tions found in the PLS models of sourness and saltiness, respectively 0.76
and 0.84, are acceptable, but the correlations between the other two sensory
taste attributes, sweetness and umami, and the ETSPU are low. RMSECV
values are low except for the prediction model of sweetness. This is re-
flected in the RPD value of the prediction model. All models show RPD
values higher than 2, except for the model for sweetness which has an RPD
value of 1.4. The correlation loadings plot (Figure 6.3) shows that sweetness
is positively correlated with sensors 8, 9, 10 and 11, which are the cationic
sensors. A negative correlation exists between sweetness and sensors 1, 2, 5
Relation between sensory analysis and instrumental measurements 187
and 7, which are all anionic sensors. Since more than one sensor is correlated
with sweetness, the question of sensor selection rises. Sourness is correlated
negatively with sensor 16. By selecting only two sensors for sweetness and
sensor 16 for sourness, the main taste attributes of tomatoes could be pre-
dicted. The possibilities of sensor selection for the prediction of sweetness
were evaluated in a PLS1 analysis. Using only sensor 1 and sensor 10, a
prediction model for sweetness with a correlation of 84%, RMSECV of 0.88
and RPD value of 2.4 is created. This result is better than when all sensors
of the ETSPU are used. The two other tastes, saltiness and umami, are not
highly correlated to any of the sensors of the ETSPU.
Sensor 3
Sensor 4
Sensor 6
Sensor 8
Sensor 10Sensor 11Sensor 15
Sensor 16
Sweetness
Umami0 0
0.2
0.4
0.6
0.8
1.0
PC
2
PC 1
Sensor 1Sensor 2
Sensor 5 Sensor 7
Sensor 9
Sensor 17
Sourness
Saltiness
-1.0
-0.8
-0.6
-0.4
-0.2
0.0-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Figure 6.3: Correlation loadings of the first two PC’s based on the sensory panel
scores and measurements with the ETSPU (X-expl. (82%, 10%); Y-expl. (47%,
18%)).
188 6.3 Results
ZZ
BA
BB
CA
GA
HA
Sweetness
Sourness
SaltinessUmami
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC
2
PC 1
Figure 6.4: Correlation loadings of the first two PC’s based on the sensory panel
scores and measurements with the ASTREE ET (X-expl. (78%, 16%); Y-expl.
(36%, 14%)).
A PLS2 analysis was also performed on the data from the ASTREE ET
(Table 6.7). The results of the PLS calibration models between the sensory
taste attributes and the ASTREE ET (results not shown) are comparable
to those of the ETSPU. All slopes and correlations are again close to 1 and
offsets and RMSEC values are low. The validation models, however, are
different from those of the multisensor system developed at the University
of Saint-Petersburg. The slopes are low and the offsets are high, but the
correlations between the ASTREE ET and sweetness, sourness, saltiness and
umami are high, respectively 0.80, 0.70, 0.94 and 0.85. All RMSECV values
are low, except for the prediction model of sweetness. This is again reflected
in the RPD values. The model for sweetness has a value of 1.7, while all
other models have RPD values higher than 2. The correlation loadings plot
(Figure 6.4) shows that sweetness is positively correlated with sensor CA
Relation between sensory analysis and instrumental measurements 189
and negatively with sensor GA. Sourness shows a negative correlation with
sensor BB. Saltiness and umami are not highly correlated to any of the
sensors of the ASTREE ET.
1751
1689
1612
1542
1450 13731349
Sweetness
Sourness
Saltiness
Umami13261265
1218
11031018
-1.0
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
PC 1
PC
2
Figure 6.5: Correlation loadings of the first two PC’s based on the sensory panel
scores and measurements with SIA-ATR-FTIR (X-expl. (97%, 2%); Y-expl. ( 52%,
19%)).
The main results of the PLS2 analysis in which the absorbances at se-
lected wavenumbers of the first derivative SIA-ATR-FTIR spectra are used
to predict sensory panel scores are listed in Table 6.7. As with the ET
systems, four PC’s were needed for the model. The correlations between
the panel scores and the predicted sweetness and sourness are high. Both
the calibration (results not shown) and validation models for sweetness and
sourness show correlations equal to or higher than 0.90. The RMSEC and
RMSECV values are low and RPD values are high for all studied sensory
taste attributes. The prediction models for umami and saltiness are some-
what less accurate with lower correlations between the measured and pre-
190 6.4 Discussion
dicted score but still acceptable. This is explained by the fact that only a
small part of the scale was used to score by the panelists, indicating that the
differences in umami and salty taste between the cultivars are small. The
RPD values of the prediction models of both umami and saltiness, however,
are higher than 2. The PLS2 results indicate that while FTIR seems to be
a very accurate technique to predict sweetness and sourness, the ET sys-
tems give the best predictions of saltiness. The correlation loadings plot
(Figure 6.5) shows that sweetness and umami are both correlated with 1103
cm−1 and 1018 cm−1, 1450 cm−1, 1373 cm−1, 1349 cm−1, 1326 cm−1 and
1265 cm−1. At these wavenumbers important C-O stretching vibrations are
found. Saltiness shows some correlation with 1612 cm−1 and 1542 cm−1 and
sourness is correlated with wavenumbers 1689 cm−1 and 1751 cm−1, which
are situated in IR region with C=O stretching vibrations.
6.4 Discussion
6.4.1 Addition of chemical compounds to a tomato juice
The correlations and interactions of taste compounds to the four sensory
taste attributes of tomatoes were studied in a first experiment. Hereto,
different amounts of fructose, citric acid, NaCl and a taste enhancer Ve-
Tsin were added to a weak tasting tomato juice.
The perception of sweetness is in general influenced by the level of fruc-
tose, citric acid and glutamate in the samples. Stevens et al. (1977) and
Fernandez-Ruiz et al. (2004) found that when the total sugar content of
a food product is low, citric acid reduces the perceived sweetness. But in
food products with high sugar concentrations, however, citric acid has a
sweetness increasing effect. In the experiment described in this thesis, the
strongest perception of sweetness was found at high fructose and high citric
acid levels. Also, the presence of some salts is found to intensify sweetness
(Stevens et al., 1977; Stevenson et al., 1999). The presence of 8% salt in
the taste enhancer Ve-Tsin could be an explanation for the positive effect of
Relation between sensory analysis and instrumental measurements 191
this taste enhancer on sweetness.
The sourness as perceived by the panelists is related to the level of citric
acid and the interaction of citric acid and fructose. The fructose content
of the sample greatly affects sourness. Stevens et al. (1977) and Baldwin
et al. (1998) described that in case of samples with a low citric acid and
glucose content, fructose is found to reduce sour taste. With high citric
acid and glucose concentrations, this effect is not valid. According to the
authors, sugars have a much less intense effect on the perception of sourness
than acids on sweetness. Despite the effect of citric acid on sweetness and
of fructose on sourness, both tastes can be measured perfectly in tomato
samples using a trained sensory panel.
The perception of saltiness in tomatoes seems to be positively affected
by the citric acid and NaCl content of the samples. The role of citric acid
can be explained by the fact that saltiness and sourness are detected by a
common family of proteins (Ramos Da Conceicao Neta et al., 2007).
Finally, only NaCl seems to affect the perception of umami significantly.
This is an odd result, which could maybe be explained by the inconsistency
between the training of the panelists and the experiment. During the train-
ing, a taste enhancer from Delhaize was used to train the panelist in tasting
umami. Since this taste enhancer was no longer available, an alternative
was found in the taste enhancer Ve-Tsin from a Chinese restaurant. The
two taste enhancers contained different amounts of monosodium glutamate
and salts, which could explain the difficulties of the panelists to taste both
saltiness and umami. This hypothesis, however, has not been confirmed yet.
6.4.2 Tomatoes with a wide range of tastes
Using a trained sensory panel, it is possible to distinguish between tomato
cultivars. The cultivars are classified according to their taste and taste com-
pounds. The results of the sensory panel and the reference analysis with
EHT and AAS can be related to each other. Amoroso, a sweet tomato
which also contains high sugar concentrations, is classified separately from
192 6.4 Discussion
the other cultivars. The separation occurs based on the taste and chemical
composition of this cultivar. The cultivar Admiro is highly correlated with
sourness as perceived by the sensory panel. This perceived sourness, how-
ever, can not be explained directly by the chemical composition of the cul-
tivar. Admiro does not contain a high concentration of citric acid. Stevens
et al. (1977) and Baldwin et al. (1998) described that in food products with
a low citric acid and glucose content, which is the case for Admiro, fructose
is found to reduce sour taste. Since the fructose content of this cultivar is
also low, this will not affect the sourness significantly. The content of malic
acid, on the other hand, could be an explanation for the sourness of Admiro.
Malic acid is found to be up to 14% more sour (w/v) than citric acid (Stevens
et al., 1977). The relatively high content of this acid in Admiro explains the
high scores given by the panelists to the attribute sourness. The other four
cultivars are not highly correlated with any of the taste attributes.
In preference mapping, the PLS2 models to predict the four taste at-
tributes which were scored by the sensory panel show different results for
the different rapid techniques. Using the reference techniques, EHT and
AAS, it is possible to predict all taste attributes. Both ET’s are able to
predict three taste attributes: sourness, saltiness and umami. The bad pre-
diction of sweetness by both ET’s is unexpected since only the ASTREE
ET failed in predicting taste compounds (Chapter 3). From this can be
concluded that the ASTREE ET is developed specifically for the prediction
of tastes and not individual taste compounds, which was also stated by the
Alpha M.O.S. company. Bleibaum et al. (2002) found good predictions of
sweetness in apple juices. Apples, however, have a higher sugar content than
tomatoes, which could explain this discrepancy. The selection of two sensors
from the array made it possible for the ETSPU to predict sweetness accu-
rately. The selection of sensors was also discussed by Legin et al. (1997),
who mentioned that the choice of an optimized sensor array is crucial for
analysis. These authors indicated that the chemical sensors need to be pre-
pared using the best sensing materials and that the correct sensors need to
be selected to allow the highest available sensitivity to certain species and
significant chemical durability and signal stability of the sensors.
Relation between sensory analysis and instrumental measurements 193
Until now, the correlation between (SIA-)ATR-FTIR and sensory panels
for the analysis of taste has not been investigated. Here, sweetness and sour-
ness are accurately predicted in tomato using SIA-ATR-FTIR, which gives
an indication of the potential of this technique to determine the main taste
of many fruit (Petro-Truza, 1987). The correlation loadings showed a high
correlation between sweetness and some wavenumbers which located in the
region of the absorbance spectrum where C-O bonds vibrate. This indicates
that sweetness is correlated with the sugars present in the samples, which
was also found from the results of the reference techniques. The prediction of
saltiness and umami using SIA-ATR-FTIR is also very good, though both
potentiometric ET systems seem to be more accurate in predicting salty
taste.
6.5 Conclusions
The potential of two newly developed instrumental techniques to predict
taste attributes as determined by a sensory panel was evaluated in this
chapter.
In a first experiment the response of the panelists to small differences in
the chemical composition of fruit was investigated. Different concentrations
of fructose, citric acid, NaCl and a taste enhancer were added to a weak
tasting tomato cultivar, Tricia, which was selected for this experiment. The
sensory panel was able to score the sweetness and sourness of the samples
accurately. High correlations were found between fructose and citric acid
on the one hand and sweetness and sourness on the other hand. Also, the
addition of a taste enhancer seemed to influence the perception of sweetness.
The determination of saltiness and umami by the panelists was correlated
with the NaCl content of the samples.
In a second experiment, the ability of two types of ET’s and SIA-ATR-
FTIR to predict taste attributes was studied using preference mapping. The
results of the rapid instrumental measurements, together with those of EHT
and AAS analysis, were compared to the results of sensory panel studies in
194 6.5 Conclusions
an experiment with six tomato cultivars. Using the reference techniques it
was possible to predict all taste attributes. High correlations were found
between the concentrations of the individual chemical compounds and the
taste attributes. Using the ETSPU and the ASTREE ET, three of the scored
tastes, sourness, saltiness and umami taste, were predicted accurately. The
prediction of sweetness by the ETSPU increased slightly after selection of a
few sensors. Using SIA-ATR-FTIR is it possible to make accurate predic-
tions on the taste of the tomatoes. Good models are found for sweetness,
sourness, saltiness and umami taste. Although the results of the prediction
of saltiness using SIA-ATR-FTIR are not as good as those of both ET’s,
FTIR showed it has potential as screening technique to analyze the taste of
a large amount of juices in a short time period. The analysis of the taste
and taste components of one sample can be reduced to less than one minute
using this rapid technique.
Chapter 7
General conclusions and
future work
7.1 General conclusions
Taste is one of the most subjective quality characteristics of food products.
Five gustatory perceptions, sweetness, sourness, saltiness, umami and bit-
terness, caused by soluble substances in the mouth describe the overall taste
of a product (Meilgaard et al., 2007). The five basic tastes are mainly caused
by the presence of sugars, organic acids, salts, monosodium-glutamate, phe-
nolics and alkaloids. Various techniques are used to determine the chemical
content of foodstuff. Sugars and acids can be determined by HPLC or
GC(-MS). Both techniques require expensive apparatus and demand a con-
siderable amount of time per measurement (Molnar-Perl, 1999). Another
method of determining specific sugars and acids is by enzymatic analysis.
For enzymatic assays, sample preparation is simple and the only apparatus
required is a spectrophotometer (Vermeir et al., 2007). The sensation of
a taste can, however, not simply be explained by the presence of a com-
pound. A traditional method for taste analysis is sensory evaluation. This
technique is used to measure those characteristics of foods and materials in
the way that they are perceived by the human senses. Although sensory
195
196 7.1 General conclusions
panel analysis is by far the most realistic technique to obtain information
on human taste perception, it has some problems including the correctness
of training, standardization of measurements, stability and reproducibility.
Other drawbacks are the high cost and taste saturation of the panelist (Meil-
gaard et al., 2007). Because of these drawbacks, there is a need to replace
traditional instrumental techniques in the analysis of taste compounds and
to relate rapid, low cost and simple methods of analysis to sensory panel
studies in food industry. In the last decade, arrays of liquid sensors, called
electronic tongues (ET’s), were developed. The main advantages of ET’s are
the low cost, easy-to-handle measurement set-up and speed of the measure-
ments (Vlasov et al., 2002). Another alternative for traditional techniques is
FTIR spectroscopy, which is a well-established technique in chemical anal-
ysis. If combined with ATR, this technique offers great advantages for food
analysis (Griffiths and de Haseth, 2007).
The objective of this work was to study the potential of rapid and objec-
tive measurement techniques for taste profiling of fruit and vegetables. This
objective was achieved through several subobjectives.
In a first step, ET technology and FTIR spectroscopy were evaluated as
rapid instrumental techniques for the classification of fruit samples based on
their chemical composition. Both the ETSPU and ATR-FTIR proved to
be able to distinguish between apple and tomato based on their chemical
composition. Differences in the main taste compounds of tomato can be
detected by both rapid techniques within minutes. An SIA system was de-
veloped and optimized to automize the acquisition of ATR-FTIR spectra.
Two types of ET’s, ETSPU and ASTREE ET, and SIA-ATR-FTIR were
compared for the classification of tomatoes with very distinct tastes. De-
spite the considerable differences in measurement protocol, both multisensor
systems and SIA-ATR-FTIR showed to be able to classify tomato cultivars
based on their sugar, acid and mineral content.
Second, the ability of the ET and (SIA-)ATR-FTIR to quantify individ-
ual taste compounds in fruit was studied. The selection of the correct set of
sensors showed to be crucial in the prediction of chemical compounds us-
General conclusions and future work 197
ing the ETSPU. After optimizing the sensor array, the ETSPU, unlike the
ASTREE ET, was able to predict individual compounds in a tomato ma-
trix. Using (SIA-)ATR-FTIR, specific vibrations of chemical bonds made it
possible to relate taste compounds directly to the absorbance spectra. The
matrix, however, seemed to influence the predictive ability of this technique
largely. ATR-FTIR, both as a static and dynamic flow through system,
proved to be accurate in predicting taste compounds.
As mentioned before, taste cannot simply be described by the chemical
composition of a food product. Therefore the potential of ET and SIA-
ATR-FTIR to determine the taste of fruit as perceived by a trained sensory
panel was examined. Using the ETSPU and the ASTREE ET three taste
attributes, sourness, saltiness and umami, were predicted accurately within
minutes. The prediction of sweetness is only acceptable using the ETSPU
after selection of specific sensors. SIA-ATR-FTIR is able to make accurate
predictions on sweetness, sourness, saltiness and umami. Although the re-
sults of the prediction of saltiness using SIA-ATR-FTIR are not as good
as those of both ET’s, FTIR showed to have potential of being a screening
technique to analyze the taste of a large amount of samples in a short time
period. This shows that the analysis of taste attributes and taste compo-
nents of one sample can be reduced to less than one minute using this rapid
technique.
And finally, after concluding that both ET technology and FTIR spec-
troscopy are good techniques to classify samples based on their taste com-
pounds and taste and to quantify taste compounds and taste, the possibilities
of the both techniques as tools for quality control of fruit juices were inves-
tigated in this thesis. Both the ETSPU and ATR-FTIR were able to group
multifruit juices and the individual syrups they are made off. However,
information of the extra compounds present in the multifruit juices is nec-
essary to make good classification models to quantify the concentrations of
constituent syrups. Individual syrups were predicted very accurately in the
multifruit juices using the ETSPU and ATR-FTIR. Both rapid techniques
made it possible to predict even very low concentrations of syrup in the
complex multifruit juice. The ET and ATR-FTIR showed to be promising
198 7.2 Future work
techniques for quality control because of their good performance, easy use
and detection speed.
7.2 Future work
In future work related to this thesis, following aspects could be addressed:
� The selection of the sensor array of the ETSPU was important both
in the prediction of individual taste compounds and taste. Future
research would involve the optimization of the sensor materials and
selection of sensors, so that good predictions could be made using a
sensor array with a minimal amount of sensors.
� To enhance the quantification potential of taste compounds using
(SIA-)ATR-FTIR, without adding an extensive separation of the com-
pound of interest, advanced statistical techniques need to be evalu-
ated. The recent development of a new class of multivariate calibra-
tion methods, called augmented classical least squares (ACLS) which
is an extension of the classical least squares model (CLS) to handle
cases where not all compounds contributing to the absorbance signal
are explicitly included in the calibration models, could make the pre-
diction of taste compounds more accurate, even when they are present
in low concentrations.
� Future research involving SIA-ATR-FTIR could involve the introduc-
tion of enzymes in the system. Using these enzymes, the individual
chemical compounds can be identified separately, making it possible
to determine compounds in low concentrations and compounds with
absorbance spectra which are similar to each other. A next step in the
development of a flow through ATR-FTIR system for high throughput
taste analysis would be to minimize the system towards a micro-total
analytical system (µ-TAS).
� After proving their potential to classify tomato cultivars, both the
ET and (SIA-)ATR-FTIR could be introduced in breeding or cultivar
General conclusions and future work 199
selection programs. Using the rapid techniques, cultivars with extreme
tastes would be identified easily, making the need for sensory analysis
less important.
� Using the rapid instrumental techniques described in this thesis, the
influence of growing techniques on taste could be studied as a practi-
cal application. The variability rising from differences in cultivation
techniques should be minimized to guarantee high quality fruit with a
constant taste. In the framework of a research project, the influence
of grafting, the distance between stems, the harvesting frequency and
the addition of salt in hydroculture on tomato taste will be studied.
� To study the potential of the ET and ATR-FTIR as rapid tools for
quality control of fruit juices, extra information on the composition
of the multifruit juices would be required. After performing measure-
ments on the extra compounds, like the aloe vera puree, vitamins
and minerals, better classification models might result with both tech-
niques.
200 7.2 Future work
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tomatoes. In: Communications in Agricultural and Applied Biological
Sciences, 10th PhD Symposium on Applied biological Sciences, Gent,
Belgium. 57−60.
Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B.M. and
Lammertyn, J. (2005). The electronic tongue and FTIR-ATR as fast
techniques for taste profiling. In: Proceedings of ISOEN, 9th Interna-
tional Symposium on Olfaction and Electronic Nose, Barcelona, Spain.
312−315.
Beullens, K., Irudayaraj, J., Kirsanov, D., Legin, A., Nicolaı, B.M. and
Lammertyn, J. (2005). The electronic tongue and FTIR for rapid taste
profiling in food. In: KVCV Voedselchemie in Vlaanderen - Trends in
Levensmiddelenanalyse, Gent, Belgium. 89−90.
Beullens, K., Sels, B.F., Schoonheydt, R.A., Nicolaı, B.M. and Lammer-
tyn, J. (2005). An optical tongue based on ATR-FTIR spectroscopy to
taste tomatoes. In: Communications in Agricultural and Applied Bio-
logical Sciences, 11th PhD Symposium on Applied biological Sciences,
Leuven, Belgium. 61−64.
Beullens, K., Sels, B.F., Schoonheydt, R.A., Nicolaı, B.M., Lammer-
tyn, J. (2006). Determination of sugars and organic acids in tomato
by means of flow injection ATR-FTIR and EMSC. In: Conference
proceedings, 10th International Conference on Flow Analysis, Porto,
Portugal. 240.
Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B.M. and
Lammertyn, J. (2007). Analysis of tomato taste using two types of
electronic tongues. In: Proceedings of ISOEN, 10th International
Symposium on Olfaction and Electronic Nose, Saint-Petersburg, Rus-
sia. 146−147.
222
Beullens, K., Meszaros, P., Kirsanov, D., Legin, A., Nicolaı, B.M. and
Lammertyn, J. (2007). Taste analysis of tomato juices using two dif-
ferent multisensor systems. In: Book of Abstracts, Euroanalysis X,
Antwerp, Belgium. 591.
Beullens, K., Vermeir, S., Nicolaı, B.M. and Lammertyn, J. (2007).
ATR-FTIR as a rapid technique for taste profiling of fruit juices. In:
Book of Abstracts, Euroanalysis X, Antwerp, Belgium. 436.
Legin, A., Kirsanov, D., Rudnitskaya, A., Beullens, K., Lammertyn, J.,
Nicolaı, B., Irudayaraj, J. and Vlasov, Y. (2005). Analysis of ap-
ple varieties - comparison of ET with different analytical techniques.
In: Proceedings of ISOEN, International Symposium on Olfaction and
Electronic Nose, Barcelona, Spain. 176−177.
Nicolaı, B. M., Beullens, K., Bobelyn, E., Hertog, M. L. A. T. M.,
Schenk, A., Vermeir, S. and Lammertyn, J. (2006). Systems to char-
acterise internal quality of fruit and vegetables. Invited lecture. In:
Purvis, A. C., McGlasson, W. B., Kanlayanarat, S. (Eds.), Acta Hor-
ticulturae : Proceedings of the Fourth International Conference on
Managing Quality in Chains. ISHS, Leuven , Belgium . 59−66.
Roth, E., Berna, A. Z., Beullens, K., Franck, C., Lammertyn, J., Schenk,
A. and Nicolaı, B. M. (2005). A comparative study of quality at-
tributes of integrated and organically produced apple fruit. In: 7th
Fruit, Nut and Vegetable Production Engineering Symposium: Infor-
mation and technology for sustainable fruit and vegetable production.
Roth, E., Berna, A. Z., Beullens, K., Schenk, A., Lammertyn, J. and
Nicolaı, B. M. (2005). Comparison of taste and aroma of integrated
and organic apple fruit. In: Communications in Agricultural and Ap-
plied Biological Sciences, 11th PhD Symposium on Applied biological
Sciences, Leuven, Belgium. 225−229.
Vermeir, S., Hertog, M.L.A.T.M., Schenk, A., Beullens, K., Nicolaı,
B.M. and Lammertyn, J. (2008). Evaluation and Optimization of
List of publications 223
High-Throughput Enzymatic Assays for Fast L-Ascorbic Acid Quan-
tification in Horticultural Products. In: The 10th World Congress on
Biosensors, Shangai, China. Accepted.