Analisis de Penalidades Cata (1)

snadePate

Received in revised form 27 March 2013Accepted 27 May 2013Available online xxxx

Keywords:CATAApplesYogurt

aims at identifying consumers ideal products and directions for product reformulation. The present workproposes the application of a penalty analysis based on consumer responses to CATA questions to identify

Larsen, & Madsen, 1996; Stewart-Knox & Mitchell, 2003). The main

product design, one key step is the selection of a product formula-tion that is aligned as much as possible with consumer sensorypreferences (van Kleef, van Trijp, & Luning, 2006). In this context,one of the main challenges for Sensory and Consumer Science isto provide actionable information for making specic changes inproduct formulation, and not just product descriptions (Moskowitz& Hartmann, 2008).

with their sensory characteristics as evaluated by a trained asses-the time and re-trained aing new pzation has

popularity in the last decade (Varela & Ares, 2012). Motrained assessors may describe the product differently to coers and/or evaluate attributes that may be irrelevant for consum-ers, consumer-driven sensory characterization of products couldhave greater external validity (ten Kleij & Musters, 2003). Thus,product optimization is increasingly being performed by askingconsumers to describe the sensory characteristics of food products.

Just-about-right (JAR) scales have been one of the rst and sim-plest consumer-based approaches to get information about theoptimum intensity of sensory attributes (Popper & Kroll, 2005).

Corresponding author. Tel.: +598 29248003.

Food Quality and Preference xxx (2013) xxxxxx

Contents lists available at

an

lseE-mail address: [email protected] (G. Ares).stages of a consumer-driven new product development processare: identication of consumer needs, development of an idea toaddress those needs, product design to substantiate the idea andthe products market introduction (Urban & Hauser, 1993). Within

sor panel (van Kleef et al., 2006). Consideringsources associated with creating and trainingpanels, particularly for specic applications durdevelopment, consumer-based sensory characteri0950-3293/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.foodqual.2013.05.014

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CATA questions to identify drivers of liking and directions for producmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqual.2013.05.014ssessorroductgainedreover,nsum-New product development has been regarded as a strategy forgaining competitive advantage and long-term nancial success(Costa & Jongen, 2006). The implementation of a market-orienta-tion and consumer-driven approach has been recognized as thebest way to develop successful products (Grunert, Baadsgaard,

development to identify the sensory attributes that drive con-sumer preferences and the characteristics of the ideal product,i.e. the product that maximize consumer liking (Lagrange &Norback, 1987). A popular approach has been the application ofpreference mapping, which consists of a group of techniques thatare able to relate consumer liking scores of a large set of productsConsumer studiesProduct optimization

1. Introductiondrivers of liking and directions for product reformulation. Two studies were conducted in which 74 and119 consumers evaluated a set of samples (5 apples and 8 yogurts) using a check-all-that-apply questionrelated to sensory characteristics and were also asked to check all the terms they considered appropriateto describe their ideal product. Data were analyzed by counting the number of consumers who did notcheck an attribute as they did for their ideal product, and its associated mean drop. A dummy variabletransformation approach was proposed to make linear regression models between CATA terms and over-all liking scores using Partial Least Squares (PLS). Juiciness, sweetness, apple avor, rmness and crispi-ness were the most relevant attributes for consumers in the apple study. Meanwhile, in the yogurt studysmoothness, homogeneity and creaminess were the main drivers of liking and were responsible for thehighest penalization on overall liking (more than 1 in the 9-point hedonic scale). PLS regression enabledthe identication of the attributes which deviation from the ideal caused a signicant decrease in overallliking. Penalty analysis on CATA questions proved to be a simple and useful approach to identify driversof liking and directions for improving the products in both studies. Advantages and disadvantages of thisapproach are discussed, as well as directions for further research.

2013 Elsevier Ltd. All rights reserved.

Over the years, many strategies have been used in new productArticle history:Received 28 August 2012

One of the most important steps of new product development process is product optimization, whichPenalty analysis based on CATA questionand directions for product reformulation

Gastn Ares a,, Cecilia Dauber a, Elisa Fernndez a, AaDepartamento de Ciencia y Tecnologa de Alimentos, Facultad de Qumica, Universidadb Instituto de Agroqumica y Tecnologa de Alimentos, Avda. Agustn Escardino 7, 46980

a r t i c l e i n f o a b s t r a c t

Food Quality

journal homepage: www.eto identify drivers of liking

Gimnez a, Paula Varela b

la Repblica, General Flores 2124, CP 11800, Montevideo, Uruguayrna, Valencia, Spain

SciVerse ScienceDirect

d Preference

vier .com/locate / foodqualt refor-

2. Materials and methods

Two studies were carried out in which consumers were asked toanswer a CATA question to describe a set of samples and their idealproduct. In the rst study consumers were asked to score their tex-ture liking and to describe the texture of eight yogurts formulatedfollowing a factorial design. In the second study consumers evalu-ated their overall liking of ve commercial apple cultivars andcompleted a CATA question which included odor, avor and tex-ture characteristics. Penalty analysis based on consumer responsesto the products compared to their ideal product was used to iden-

Consumers (n = 74) were recruited among students, professors

d Preference xxx (2013) xxxxxxIn this approach consumers are asked to evaluate a set of attributesas deviations from their ideal, by indicating if its intensity is toostrong, too weak or just-about-right (Lawless & Heymann, 2010).Penalty analysis on data from JAR has been used to identify thesensory attributes that have the largest inuence on consumer lik-ing and to identify directions for product reformulation (Plaehn &Horne, 2008). As an alternative, Xiong and Meullenet (2006) intro-duced a partial least squares (PLS) regression approach to study therelative inuence of attributes on consumer liking. Penalty analysison data from JAR scales enables the identication of the productswhich are closer to the ideal, the direction in which an attributeshould be changed if it is not in its optimum or JAR level andhowmuch liking is affected when an attribute is not JAR (Lesniaus-kas & Carr, 2004). Despite their popularity and the fact that theyprovide actionable information, the application of JAR scales inproduct optimization has raised several concerns. This type of taskcould make consumers focus on sensory characteristics that theywould not normally do (Popper & Kroll, 2005), leading to changesin their hedonic perception (Ares, Barreiro, & Gimnez, 2009; Epler,Chambers, & Kemp, 1998; Popper, Rosentock, Schraidt, & Kroll,2004).

Intensity questions have been reported to have a smaller inu-ence on consumer liking and have been recommended for productoptimization by some authors (Moskowitz, 2001; Popper et al.,2004). Considering that consumers are able to rate attribute inten-sity (Husson, Le Dien, & Pags, 2001; Moskowitz, 1996; Worch, L,& Punter, 2009) and assuming that they have an implicit ideal intheir minds (Moskowitz, 2003), Van Trijp, Punter, Mickartz, andKruithof (2007) proposed the Ideal Prole method for identifyingideal products. In this approach consumers are asked to directlyrate attribute intensity for their ideal product using unstructuredscales. Although this method has been shown to provide accuratedescriptions of ideal products that are similar to the most likedproducts (Worch, Dooley, Meullenet, & Punter, 2010; Worch, L,Punter, & Pags, 2012a, 2012b) and actionable information forproduct reformulation similar to that provided by JAR scales, itcould be difcult and not intuitive for consumers to rate the idealintensity of a large set of attributes using scales.

Check-all-that-apply (CATA) questions have been gaining popu-larity for sensory characterization of food products by consumersdue to their simplicity and ease of use (Adams, Williams, Lancaster,& Foley, 2007; Ares, Barreiro, Deliza, Gimnez, & Gmbaro, 2010;Ares, Varela, Rado, & Gimnez, 2011a; Dooley, Lee, & Meullenet,2010; Plaehn, 2012). In this approach, consumers are presentedwith a list of terms and are asked to select all the terms that theyconsider appropriate for the product. The relevance of each term isdetermined by calculating its frequency of use. CATA questionshave been reported to be a quick, simple and easy method to gath-er information about consumer perception of the sensory charac-teristics of food products; having a smaller inuence on likingscores than just-about-right or intensity questions (Adams et al.,2007).

Plaehn (2012) proposed a penalty analysis on data from CATAquestions to identify the relative importance of emotional attri-butes on overall liking scores of a set of citrus avored sodas. Con-sidering that CATA questions have been used to identify thesensory characteristics of consumer ideal product (Ares, Varela,Rado, & Gimnez, 2011b; Cowden, Moore, & Vanluer, 2009), a pen-alty analysis approach could be used to identify how much overallliking is reduced because of the deviations in sensory proles be-tween real and ideal products, as detected by a CATA question.

In this context, the aim of the present work was to identify driv-ers of liking and directions for product reformulation by applying a

2 G. Ares et al. / Food Quality anpenalty analysis based on consumer responses to CATA questionsabout a set of samples and their ideal product.

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqand workers from the School of Chemistry (Universidad de la

Table 1Formulation of the yogurts used in Study 1.

Sample Milk fatcontent (%)

Concentration of modiedstarch (%)

Concentration ofgelatin (%)

1 0.1 0 02 0.1 0 0.53 0.1 1 04 0.1 1 0.55 2.6 0 06 2.6 0 0.57 2.6 1 02.1.1. SamplesEight yogurts were formulated by modifying the fat content of

the milk, and the concentration of gelatin and modied starch (Na-tional 465, National Starch, Trombudo Central, Brasil), following a23 full factorial design. These variables have been previously re-ported to affect yogurt texture (Tamime & Robinson, 1991). Sampleformulations (Table 1) were selected in order to get a set of yogurtswith a range of different texture characteristics, based on previousstudies (Ares et al., 2007), the usual formulation of yogurts com-mercialized in the Uruguayan market, and results from preliminarytests.

Yogurts were prepared using 8% commercial sugar and 2% pow-dered skimmed milk. The rest of the formulation consisted of gel-atin, modied starch, skimmed pasteurized milk (0.1% fat content)or whole pasteurized milk (2.6% fat content), as shown in Table 1.

Yogurts were prepared using a Thermomix TM 31 (VorwerkMexico S. de R.L. de C.V., Mexico D.F., Mexico). The solid ingredi-ents were mixed with the milk, previously heated to 50 C. The dis-persion was mixed for 1 min under gentle agitation (100 rpm),heated to 90 C for 5 min and cooled to 42 C. Then, the mix wasplaced in glass containers and inoculated with 1 mL of lactic cul-tures, prepared by dispersing lyophilized cultures (Yo-Mix 205LYO 250 DCU, Danisco, France) in UHT skimmed milk to a concen-tration of 250 DCU per liter.

Fermentation was carried out in a temperature controlled ovenat (42 1) C and stopped when the sample reached a pH of 4.55(after 56 h, depending on the formulation). When the nal pHwas reached, the coagulum was broken by agitating each yogurtfor 1 min using the Thermomix TM 31 at 100 rpm. After that, yo-gurts were placed in glass containers, cooled under agitation to25 C in a water bath at 5 C, and then stored refrigerated (45 C) for 24 h, prior to their evaluation.

2.1.2. Consumer testingtify drivers of liking and directions for product reformulation.

2.1. Study 1: yogurt study8 2.6 1 0.5

TA questions to identify drivers of liking and directions for product refor-ual.2013.05.014

2.3. Data analysis

Overall liking scores were analyzed using analysis of variance(ANOVA) considering sample as xed source of variation and con-sumer as a random effect. Cluster analysis was applied on centeredand reduced overall liking scores from Study 2 in order to identifyconsumer segments with different preference patterns, consider-ing Euclidean distances and Ward aggregation.

d Preference xxx (2013) xxxxxx 3Repblica, Montevideo, Uruguay) based on their yogurt consump-tion (at least once a week) and their interest and availability to par-ticipate in the study. Their ages were 1867 years old and 64%were female. Cash incentives were not provided.

Testing was conducted in standard sensory booths under arti-cial daylight type illumination, temperature control (2224 C) andair circulation. Samples were presented in a monadic series, inclosed plastic containers labeled with three-digit random numbers,at room temperature.

Twenty grams of each sample were served to assessors at 10 Cin closed odorless plastic containers labeled with three digit ran-dom numbers. Sample presentation order followed a completedblock design balanced for carry-over and position effects. Still min-eral water was used for rinsing between samples.

Consumers were rst asked to score their texture liking using ahorizontal 9-point hedonic scale anchored at dislike very much (1)and like very much (9). Next, they completed a CATA question with16 texture terms related to texture characteristics of yogurts. Theterms were selected based on previous qualitative consumer studies(Gimnez & Ares, 2010) and were the following: smooth, viscous,homogenous, liquid, lumpy, creamy, sticky, rough, gummy, thick,gelatinous, rm, heterogeneous, consistent, runny, and mouth-coat-ing. Consumers were asked to try each yogurt sample and to checkall the terms that they considered appropriate to describe its texture.Then, consumers were asked to check all the terms they consideredappropriate to describe the texture of their ideal yogurt.

2.2. Study 2: apple study

2.2.1. SamplesFive commercial apple cultivars available in Uruguay were

used: crisp pink, fuji, granny smith, red delicious and royal gala.All were provided by a fruit and vegetable wholesale supplier lo-cated in Montevideo, Uruguay. Apples were removed from a coolstorage room at 5 C 24 h prior to testing and placed at room tem-perature. Each fruit was cleaned with a wet cloth and cut intoquarters approximately 5 min before tasting. If any bruising or vi-sual defect was observed the sample was discarded.

2.2.2. Consumer testingConsumers (n = 119) were randomly recruited among people

walking through the City Hall of Montevideo (Uruguay) based ontheir apple consumption (at least once a week) and their interestin participating. Their ages were 1875 years old and 67% were fe-male. Cash incentives were not used.

Testing was conducted in standard sensory booths under arti-cial daylight type illumination, temperature control (2224 C) andair circulation. Samples were presented monadically, in plasticcontainers labeled with three-digit random numbers, at room tem-perature. Sample presentation order followed a completed blockdesign balanced for carry-over and position effects. Water wasavailable for rinsing between samples.

Consumers were rst asked to score their overall liking using ahorizontal 9-point hedonic scale anchored at dislike very much (1)and like very much (9). Next, they completed a CATA question with15 terms related to sensory characteristics of apples. Consumers wereasked to try the sample and then to check all the terms that they con-sidered appropriate to describe each apple. The terms were selectedbased on previous literature (Andani, Jaeger, Wakeling, & MacFie,2001; Daillant-Spinnler, MacFie, Betys, & Hedderley, 1996; Jaeger,Andani, Wakeling, & MacFie, 1998) and preliminary consumer stud-ies. The terms considered in the CATA question included texture, a-vor and odor characteristics: rm, sour, odorless, juicy, crispy,

G. Ares et al. / Food Quality antasteless, sweet, avorsome, mealy, bitter, coarse, apple avor, appleodor, soft and astringent. After testing each sample, consumers wereasked to complete the CATA question to describe their ideal apple.

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqFrequency of use of each sensory attribute was determined bycounting the number of consumers that used that term to describeeach sample. Cochrans Q test (Manoukian, 1986; Parente, Manzon-i, & Ares, 2011) was carried out to identify signicant differencesbetween samples for each of the terms included on the CATA ques-tion. In Study 2, Fishers exact test (Fisher, 1954) was used to deter-mine signicant differences between clusters in the frequency ofuse of each term for describing the ideal product.

Correspondence analysis (CA) was used to get a bi-dimensionalrepresentation of the samples and the relationship between sam-ples and terms from the CATA question. This analysis was per-formed on the frequency table containing the samples in rowsand the terms from the CATA question on the columns. The idealproduct was considered as supplementary individual in the analy-sis. This option is available in R language.

A multiple factor analysis for contingency tables (MFACT) wasused to investigate the relationship between responses to the CATAquestion of the two consumer groups identied in the cluster anal-ysis (Bcue-Bertau & Pags, 2004). The frequency table of each con-sumer segment was considered as a separate group of variables inthe analysis. RV coefcient between the congurations of bothclusters was also calculated.

Penalty analysis was carried out on consumer responses todetermine the drop in overall liking associated with a deviationfrom the ideal for each attribute from the CATA question. CATAdata is usually coded as binary data assigning 1 or 0 if a term ischecked or not checked to describe a product, respectively. In thepresent work, a dummy variable approach was used to describeif an attribute was used to describe the product as in the idealproduct (0) or differently (1). Therefore, for each attribute the per-centage of consumers who used it differently for describing eachproduct and the ideal was determined, as well as the mean dropin liking associated with that deviation from the ideal. A one factorKruskalWallis test was performed for each CATA variable as thefactor and overall liking as dependent variable, in order to deter-mine if deviation from the ideal for each attribute caused a signif-icant decrease in overall liking (Plaehn, 2012).

Furthermore, a partial-least squares (PLS) regression was usedto estimate the weight of the deviation from the ideal of each termfrom the CATA question, following a similar approach to that pro-posed by Xiong and Meullenet (2006). In this model absolute likingscores were considered as dependent variable and the dummyvariables indicating if consumers described the product differentfrom their ideal as regressors. Only attributes which were consid-ered as deviated from the ideal for at least 20% of the consumerswere considered, as suggested by Xiong and Meullenet (2006)and Plaehn (2012).

Table 2Mean texture liking scores and standard deviations (between brackets) for the yogurtsamples evaluated in Study 1.

Sample

1 2 3 4 5 6 7 8

Texture liking(n = 74)

4.2c,d

(2.1)5.6a

(1.9)3.5d

(2.2)5.2a,b

(1.9)5.6a

(2.1)5.9a

(1.9)4.4b,c

(2.3)5.3a,b

(1.9)Mean texture liking scores with different superscripts are signicantly differentaccording to Tukeys test for a condence level of 95%.


ing at Table 3 it seems clear that it is necessary to increase the

smoothness. As shown in Table 3 deviation from the ideal in thoseattributes was associated with a lower frequency of mention thatcan be linked to a lower intensity, when compared to the ideal yo-gurt. It is worth mentioning though, that in some products thereare some attributes never perceived as enough by consumers,among which creaminess is a typical example. In a product wherecreaminess is characteristic (ice-cream, sauces, soups, etc.) theymight always report as not creamy enough. This fact has beenfound when consumers use JAR scales (Moskowitz, 2003; Roth-man, 2007), and would probably have an inuence when usingother kind of scales or even CATA questions, when consumers rateproducts comparing with their expectations for an ideal productthat have in their minds.

Regression coefcients of the PLS regression models for theeight yogurt samples are shown in Table 4. For all samples devia-tion from the ideal signicantly affected overall liking for only asubset of attributes. Smoothness was the only attribute in whichdeviation from the ideal signicantly decreased overall liking forall samples. For sample 1, overall liking scores signicantly de-creased because smoothness, lumpiness, homogeneity, roughness,mouth-coating, heterogeneity and its liquid consistency deviatedfrom the ideal. Meanwhile, in the case of sample 2 overall likingsignicantly decreased due to the deviation from the ideal insmoothness, homogeneity and creaminess.

According to Xiong and Meullenet (2006) one of the mainadvantages of PLS-based penalty analysis is information aboutthe maximum potential improvement on overall liking, which iscalculated as the difference between the models intercept and ac-tual mean liking score. Although the condence interval of theintercept can be broad, the estimation of the maximum potential

d Prhomogeneity and thickness of this sample since the frequency ofuse of these attributes to describe sample 1 was lower than forthe ideal product. Furthermore, in the case of sample 3 the mainsensory problems were associated with its smoothness, lumpiness,All signicance test were done at a signicance level of 0.05.Statistical analyses were performed using XLStat 2009 (Addinsoft,Paris, France) and R language (R Development Core Team, 2007)using FactoMineR (L, Josse, & Husson, 2008).

3. Results

3.1. Study 1: yogurt samples

3.1.1. Texture liking scoresSignicant differences in the texture liking scores of the yogurt

samples were found (F = 13.19, p < 0.0001). As shown in Table 2,average texture liking scores were low, ranging from 3.5(SD = 2.2) to 5.9 (SD = 1.9). Samples 2, 4, 5, 6 and 8 had the highestoverall liking scores (5.25.9), whereas samples 1 and 3 were theleast preferred by consumers.

3.1.2. CATA countsSignicant differences (p 6 0.05) in the frequency with which

14 out of the 16 terms of the CATA question were used to describethe yogurt samples, suggesting that consumers perceived differ-ences in the sensory characteristics of the evaluated yogurts (Ta-ble 3). The ideal yogurt was described as smooth, homogeneous,creamy, consistent and thick, which indicates that these were themain drivers of liking for this type of product, in agreement withPohjanheimo and Sandell (2009) and Bayarri, Carbonell, Barrios,and Costell (2011).

According to their texture, samples were sorted into three maingroups, as shown in sample representation in the rst and seconddimensions of the CA (Fig. 1). A rst group of yogurts, composed ofsamples 3 and 7, were located at positive values of the rst dimen-sion and negative values of the second dimension, being mainlydescribed as heterogeneous, lumpy and rough. These two sampleshad a similar formulation and only differed in their fat content;they both included 1% of modied starch and did not include gel-atin. Samples 1 and 5 were located at positive values of the rstand second dimension and were described as runny and liquidby consumers; which could be explained by the fact that thesesamples did not include modied starch and gelatin in their formu-lation (Table 1). Finally, samples 2, 6, 4 and 8, which were formu-lated with 0.5% gelatin, were located at negative values of thesecond dimension and were described as thick, consistent, rmand gelatinous.

As shown in Fig. 1, the ideal yogurt was characterized by theterms smoothness, creaminess and homogeneity. As expected,the position of the ideal product was close to the samples whichshowed the highest texture liking scores and relatively far fromthe least preferred samples (Table 2).

3.1.3. Penalty analysisFig. 2 shows the mean drops in texture liking as a function of

the proportion of consumers that checked an attribute differentlythan for the ideal product for three yogurt samples. As shown,the penalty analysis enabled the identication of directions forproduct improvement for each of the samples. In the case of sam-ple 1, the attributes with the highest mean drop and deviationfrom the ideal were Homogeneous, Consistent and Thick. By look-

4 G. Ares et al. / Food Quality anhomogeneity and creaminess, which made it largely deviate fromthe ideal yogurt. Finally, for sample 6 the percentage of consumerswho stated that the attributes deviated from the ideal was lower

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqthan for samples 1 and 3, in agreement with the higher overall lik-ing score of the former sample. The main deviations from the idealand penalties for this sample were related to its creaminess and

Table 3Frequency (%) with which the terms of the CATA question were used by consumers todescribe the eight yogurt samples and their ideal product, and results from CochransQ test for comparison between samples.

Attribute Sample

Ideal 6 5 2 8 4 7 1 3

Smooth*** 92 64 62 53 45 38 23 41 12Creamy** 86 38 35 35 38 36 32 16 18Homogeneous*** 80 57 26 39 43 49 5 20 8Consistent*** 41 45 11 45 55 57 20 0 9Thick*** 38 43 8 32 51 49 30 3 23Firm*** 20 45 1 36 65 47 8 0 1Runny*** 18 5 47 11 0 3 15 55 20Viscousns 12 12 14 8 15 7 7 5 18Mouth-coating* 9 19 14 11 16 16 24 15 30Liquid*** 3 1 45 4 0 3 22 73 23Heterogenous 3 7 18 19 0 4 42 32 49Lumpy*** 1 11 26 7 8 11 61 32 57Gelatinous*** 0 22 0 30 26 31 0 1 4Stickyns 0 4 3 4 8 3 8 3 14Rough*** 0 7 9 5 11 16 46 24 46Gummyns 0 1 1 0 5 5 7 1 4

Samples are arranged in descending texture liking order from left to right.*** Indicates signicant differences between samples according to Cochrans Q testat p 6 0.001.** Indicates signicant differences between samples according to Cochrans Q testat p 6 0.01.* Indicates signicant differences between samples according to Cochrans Q test atp 6 0.05.ns Indicates no signicant differences between samples according to Cochrans Qtest (p 6 0.05).

eference xxx (2013) xxxxxximprovement is usually in agreement with average liking scores.As shown in Table 4, the maximum potential increase in overall lik-ing if the attributes that deviated from the ideal were modied


Fig. 1. Representation of the yogurt samples, the ideal product and the terms in the rst and second dimensions of the correspondence analysis of the CATA counts of Study 1.

Fig. 2. Mean drops in overall liking as a function of the percentage of consumers that checked an attribute differently than for the ideal product for three of the yogurtsamples of Study 1. Attributes highlighted in bold correspond to those in which more than 20% of the consumers considered that it deviated from the ideal and caused asignicant decrease in texture liking according to KruskalWallis test for a 95% condence level.

G. Ares et al. / Food Quality and Preference xxx (2013) xxxxxx 5

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CATA questions to identify drivers of liking and directions for product refor-mulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqual.2013.05.014

al fo

mp

d PrTable 4Percentage of consumers (%) who describe each yogurt sample as different from the ideintercept of PLS models.

Term Sample 1 Sample 2 Sample 3 Sa

6 G. Ares et al. / Food Quality anranged from 1.3 to 3.0. This information enables to take decisionsfor product reformulation based on the potential gain in consumeroverall liking scores.

By combining Table 4 with the description of the samples andthe ideal yogurt presented in Table 3, it is possible to identify rec-ommendations for product improvement for each of the evaluatedsamples. Lumpiness was the attribute with the highest regression

% RC % RC % RC %

Smooth 62 0.15 50 0.24 82 0.17 59Lumpy 31 0.31 8 55 0.10 12Viscous 18 12 16 14Homogeneous 65 0.13 49 0.18 77 0.08 39Liquid 73 0.14 4 26 0.09 5Thick 38 ns 32 ns 34 ns 46Gelatinous 1 30 ns 4 31Firm 20 ns 41 ns 22 ns 41Sticky 3 4 14 ns 3Creamy 73 ns 57 0.18 69 0.10 58Rough 24 0.17 5 46 0.09 16Consistent 41 ns 45 ns 39 ns 41Mouth-coating 22 0.13 12 34 0.10 20Gummy 1 0 4 5Runny 51 ns 23 ns 30 0.09 18Heterogenous 35 0.15 22 ns 49 0.12 7Intercept 7.2 7.2 6.3 7Mean texture liking 4.2 5.6 3.5 5Mean drop* 3.0 1.8 2.8 1

: Indicates that the attribute was not included in the PLS model because less than 20% osignicant coefcients.* Mean drop is calculated as the intercept of the model minus the actual texture liking

Table 5Summary of the recommendations for reformulating the texture of the eight yogurtsconsidered in Study 1, based on results from PLS modeling (Table 4) and consumerresponses to the CATA question (Table 3).

Sample Main recommendations for reformulation

1 Reduce lumpiness and roughness. Increase smoothness,homogeneity and thickness (to reduce deviation in liquid)

2 Increase smoothness, homogeneity and creaminess3 Increase smoothness, homogeneity, consistency and creaminess.

Reduce lumpiness and roughness and heterogeneity4 Increase creaminess, smoothness and homogeneity5 Increase smoothness and consistency (to reduce deviation in

Liquid). Reduce lumpiness6 Increase smoothness, homogeneity, creaminess. Reduce

consistency7 Increase smoothness, creaminess and homogeneity. Reduce

rmness, roughness, mouth-coating and heterogeneity8 Reduce rmness, consistency and viscosity. Increase smoothness

and creaminess

Table 6Mean overall liking scores and standard deviations (between brackets) for the applecultivars evaluated in Study 2, at the aggregate level and for the two consumersegments identied using Cluster analysis.

Sample Global (n = 119) Cluster 1 (n = 79) Cluster 2 (n = 40)

Crisp pink 7.2b (2.1) 7.7c (1.9) 6.3b (2.2)Fuji 7.1b (2.1) 7.4c (1.9) 6.1b (2.1)Granny smith 5.7a (2.5) 6.4b (2.3) 4.2a (2.3)Red delicious 6.2a (2.6) 5.2a (2.3) 8.2c (1.1)Royal gala 5.7a (2.3) 5.2a (2.2) 6.7b (1.9)

Mean overall liking scores with different superscripts are signicantly differentaccording to Tukeys test for a condence level of 95%.

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqr each of the attributes included in the CATA question, regression coefcients (RC) and

le 4 Sample 5 Sample 6 Sample 7 Sample 8

RC % RC % RC % RC % RC

0.21 41 0.16 41 0.20 77 0.14 53 0.14 27 0.15 12 59 ns 9 20 ns 14 16 22 0.150.17 59 ns 28 0.16 74 0.10 36 ns 45 0.18 4 24 ns 3 ns 35 ns 41 ns 32 ns 43 nsns 0 22 ns 0 26 nsns 19 46 ns 26 0.14 55 0.15 3 4 8 ns 8 0.32 59 ns 51 0.16 57 0.19 57 0.35

eference xxx (2013) xxxxxxcoefcient in the PLS model of sample 1, suggesting that it wasthe main attribute to be modied to improve it. Similarly, in thecase of sample 8, creaminess was the attribute with the highestregression coefcient. Considering that this attribute was less fre-quently used to describe this sample than the ideal product, itwould be recommended to increase its creaminess. For some sam-ples (samples 3, 5, 6 and 7) there were several attributes with sim-ilar weights with regard to their inuence on texture liking. Asummary of the recommendations for improvement of each prod-uct is shown in Table 5.

3.2. Study 2: apple samples

3.2.1. Overall liking scoresSignicant differences in the overall liking scores of the apple

cultivars were found (F = 12.34, p < 0.0001). As shown in Table 6,

9 7 46 0.14 11 ns 38 ns 39 0.17 39 ns 45 0.18ns 18 15 28 0.11 18 1 1 7 5 35 0.13 23 ns 27 0.11 18 18 9 45 0.20 3 .0 6.9 7.3 7.3 7.4.2 5.6 5.9 4.4 5.3.8 1.3 1.4 2.9 2.1

f the consumers considered that it deviated from the ideal; ns: corresponds to non-

score.

Table 7Frequency (%) with which the terms of the CATA question were used by consumers todescribe the ve apple cultivars and their ideal apple, and results from Cochrans Qtest for comparison between samples.

Attribute Sample

Ideal Crisppink

Fuji Reddelicious

Royalgala

GrannySmith

Juicy*** 92 63 76 48 51 49Firm*** 79 68 70 18 19 66Sweet*** 77 32 39 61 31 5Flavorsome*** 76 43 44 31 25 25Apple avor*** 69 45 40 37 25 14Crispy*** 64 66 55 11 16 46Apple odor*** 39 13 8 8 5 8Sour*** 22 52 12 3 7 80Astringent*** 7 8 7 1 3 16Soft*** 6 1 2 45 49 2Mealy*** 5 1 0 58 36 1Coarse*** 3 3 1 24 15 2Bitter*** 2 5 10 3 6 18Odourless*** 1 13 14 14 22 14Tasteless*** 0 4 9 10 31 8

Samples are arranged in descending texture liking order from left to right.*** Indicates signicant differences between samples according to Cochrans Q testat p 6 0.001.


on the aggregate level overall liking scores ranged from 5.7(SD = 2.5) to 7.2 (SD = 2.1); being crisp pink and fuji the preferredcultivars.

Cluster analysis on overall liking scores enabled the identica-tion of two consumer segments with different preference patterns.Cluster 1 was composed of 79 consumers who clearly preferredcrisp pink and fuji apples and rejected royal gala and red delicious(Table 6). On the other hand, the remaining 40 consumers (Cluster2) preferred red delicious apples, rating their overall liking using anaverage score higher than 8, whereas they disliked slightly grannysmith.

3.2.2. CATA countsSignicant differences (ph 0.001) were found in the frequency

with which all the terms included in the CATA question were usedto describe the apple samples, suggesting that consumers per-

ceived large differences in the sensory characteristics of the evalu-ated apple cultivars (Table 7).

Sample representation in the rst and second dimensions of theCA showed that according to both consumer segments the appleswere sorted into three groups (Fig. 3). A rst group, located atnegative values of the rst and second dimension, was composedof rm and crispy apples, crisp pink and fuji. Royal gala and reddelicious formed a second group, being described as mealy, softand coarse by both clusters. Finally, granny smith apples werelocated in a distinct position due to their sourness, bitternessand astringency. Despite the sensory maps of the samples (RVcoefcient = 0.91, ph 0.0001) and their general descriptionwere similar, the clusters differed in the location of their idealapple and in how they used some of the terms of the CATAquestion.

As shown in Fig. 3, the location of the ideal apple for Clusters 1and 2 was clearly different. For consumers in Cluster 1 the sensory

G. Ares et al. / Food Quality and Preference xxx (2013) xxxxxx 7Fig. 3. Representation of the samples, the ideal apple and the terms in the rst and secotwo identied consumer segments with different preference patterns: (a) Cluster 1 (n =

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqnd dimensions of the correspondence analysis of the CATA counts of Study 2 for the79) and (b) Cluster 2 (n = 40).


characteristics of the ideal apple were similar to those of cultivarsfuji and crisp pink, whereas for Cluster 2 the ideal apple was inter-mediate between Fuji and Red delicious, being closer to the latter.The location of the ideal products for both consumer segments wasin agreement with overall liking scores. The difference in the loca-tion of the ideal apple of both consumer segments could be ex-plained by their responses to the CATA question. As shown in

nicantly more frequently the terms rm, sour and crispy, and sig-nicantly less frequently the term soft than consumers in Cluster 2,which indicates differences in their drivers of liking. Cluster 1 pre-ferred rmer, crisper and more sour apples than consumers inCluster 2, as shown in Table 6 and Fig. 3. Cluster 1 clearly preferredfuji and crisp pink apples, which were characterized by their rm-ness and crispiness; whereas they rejected red delicious and royalgala apples which were described as soft and mealy. The oppositetrend was found for Cluster 2. Daillant-Spinnler et al. (1996) alsofound consumer segmentation when testing 12 southern-hemi-sphere varieties of apples, with patterns according to whether asweet, hard apple or a juicy, acidic apple was preferred.

Regarding the use of the terms from the CATA question, the rep-resentation of the terms in the rst and second dimension of theMFACT showed that the clusters differed in the way in which theydescribed the samples; in particular in how they used terms someof them related to complex sensory attributes, such as Apple avor,avorsome, apple odor, and tasteless (Fig. 4). These attributes forboth clusters were located far from each other, suggesting thatthey were used differently. As shown in Fig. 3, consumers in bothsegments associated avor and odor intensity with their preferredapple cultivars. The terms avorsome and apple avor were asso-ciated with crisp pink and fuji for Cluster 1, whereas they wereassociated with royal gala and red delicious for Cluster 2. A similartrend was found for the term tasteless, which was associated withred delicious and royal gala apples for Cluster 1 and with grannysmith for consumers in Cluster 2. On the other hand, it is interest-ing to highlight that the rest of the terms of the CATA question,which corresponded to simplest sensory attributes, were locatedclose for both clusters, suggesting that they were used in a similar

Table 8Frequency (%) with which the terms of the CATA question were used by the twoidentied consumer segments to describe their ideal product and signicance atwhich signicant differences existed according to Fishers exact test.

Term Frequency of use (%) p Fishers exact test

Cluster 1 (n = 79) Cluster 2 (n = 40)

Juicy 92 93 >0.999Firm 89 60 0.001Sweet 76 80 0.653Flavorsome 80 68 0.176Apple avor 67 60 0.676Crispy 75 43 0.001Apple odor 41 38 0.844Sour 29 8 0.009Astringent 9 93 0.265Soft 0 18 0.001Mealy 4 8 0.662Coarse 1 5 0.545Bitter 1 3 >0.999Odorless 0 3 0.336Tasteless 0 0 1

Terms highlighted in bold correspond to those in which signicant differences intheir frequency of use between Clusters existed according to Fishers exact test.

8 G. Ares et al. / Food Quality and Preference xxx (2013) xxxxxxTable 8, signicant differences between clusters were identiedin the frequency with which 4 terms of the CATA question wereused to describe the ideal apple. Consumers in Cluster 1 used sig-Fig. 4. Representation of the terms from the CATA question in the rst and second dimensidentied consumer segments with different preference patterns.

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqway by consumers in both clusters (Fig. 4). The RV coefcient be-tween term congurations for both clusters was 0.65(p = 0.0006), higher than the RV coefcient between sampleions of the multiple factor analysis performed on CATA counts of Study 2 for the two


congurations (RV = 0.91). This suggests that although both clus-ters did not differ in the perception of similarities and differencesamong apple cultivars, they differed in the way in which they usedsome of the terms to describe them.

3.2.3. Penalty analysisFig. 5 shows the mean drops in overall liking as a function of the

proportion of consumers that checked an attribute differently thanfor the ideal apple across all samples for the two consumer seg-ments identied in cluster analysis. Except for apple odor, at theaggregate level deviation from the ideal of all the attributes in-cluded in the CATA question caused a signicant drop in overallliking for consumers in Cluster 1. The attributes that caused thehighest decrease in overall liking were tasteless, coarse, soft,mealy, juicy and rm, which indicates that texture attributes hadthe highest relevance for the hedonic perception of these consum-ers. On the other hand, avor attributes were the most relevant forconsumers in Cluster 2, who penalized samples which deviatedfrom the ideal in sweetness, taste intensity and bitterness. It is alsointeresting to highlight that deviation from the ideal in the termsrm, crispy, mealy and coarse did not cause a signicant drop inoverall liking for Cluster 2, meaning that most probably consumersin this group would be prepared to sacrice texture in favor of theirpreferred apple taste.

Regression coefcients of the PLS regression models for the veapple samples and the two consumer segments are shown in Ta-ble 9. For all samples deviation from the ideal signicantly affectedoverall liking for only a subset of attributes. Moreover, clear differ-ences were identied between the clusters. For example, Cluster 1

signicantly decreased overall liking scores for crisp pink applesdue to the deviation from the ideal in rmness, juiciness andsweetness; whereas Cluster 2 penalized deviation from the idealin juiciness, sweetness, sourness and avorsome.

By looking at the maximum potential increase in overall liking ifthe attributes that deviated from the ideal were modied, it seemsclear that it is not worth it to suggest improvements in the sensorycharacteristics of fuij and red delicious apples for consumers inCluster 1 and Cluster 2, respectively.

Furthermore, by studying Table 9 together with the descriptionof the samples, their ideal apple (Table 7) and the overall liking rat-ings (Table 6), it is possible to identify recommendations for prod-uct improvement for consumers in Cluster 1 and 2. Consumers inCluster 1 preferred crisp pink and fuji apples (Table 6). Juicinesswas the attribute which deviation from the ideal had the highestweight in decreasing overall liking for crisp pink apples, indicatingthat this cultivar would be more liked by these consumers byincreasing its juiciness (Table 9). Meanwhile, the main directionfor improvement in the case of fuji apples for consumers in Cluster1 was related to the term tasteless, which indicates the need for anincrease in avour intensity. On the other hand, consumers in Clus-ter 2 clearly preferred red delicious apples, which could be im-proved by increasing its sweetness and reducing its softness(Tables 7 and 9). However, the improvement in these last cultivarsfor Cluster 1 and Cluster 2 would not lead to a large increase inoverall liking scores, as previously discussed. For consumers inCluster 2 it would be recommended to improve royal gala applesby increasing its sweetness and juiciness and reducing its softness.These changes would lead to a potential increase in overall liking of

G. Ares et al. / Food Quality and Preference xxx (2013) xxxxxx 9Fig. 5. Mean drops in overall liking as a function of the percentage of consumers that chetwo identied consumer segments with different preference patterns. Attributes highlisignicant decrease in overall liking according to KruskalWallis test for a 95% conden

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqcked an attribute differently than for the ideal product at the aggregate level for theghted in bold correspond to those in which deviation from the ideal and caused ace level.


l fo

Gran

Clus

%

4453763247694964222331242464

d PrTable 9Percentage of consumers (%) who describe each apple sample as different from the ideaintercept of PLS models for the two consumer segments identied in Cluster analysis.

Term Crisp pink Fuji

Cluster 1 Cluster 2 Cluster 1 Cluster 2

% RC % RC % RC % RC

Firm 42 0.13 35 ns 38 ns 30 nsJuicy 45 0.31 53 0.23 37 0.16 35 nsSweet 59 0.16 70 0.23 50 0.13 70 0.19Bitter 23 ns 10 27 0.18 10 Apple odor 47 ns 40 ns 48 ns 33 nsSour 49 ns 65 0.17 43 ns 15 Crispy 36 ns 40 ns 49 0.13 40 0.19Flavorsome 49 ns 53 0.14 54 ns 58 0.22Coarse 23 ns 5 23 ns 5 Soft 22 ns 18 ns 23 ns 18 Odorless 30 ns 20 ns 30 ns 18 Tasteless 22 ns 10 27 0.29 13 Mealy 24 ns 10 24 ns 8 Apple avor 48 ns 55 ns 51 0.11 50 ns

10 G. Ares et al. / Food Quality an2.1 in the 9-point hedonic scale. A summary of the recommenda-tions for changing each apple cultivar for the two consumer seg-ments is shown in Table 10.

4. Discussion and conclusions

Sensory methodologies which aim at identifying ideal productsbased on consumer descriptions are widely used in new productdevelopment to obtain actionable directions for product improve-ment and are nowadays gaining in popularity (Worch et al., 2012a,2012b). According to Van Trijp et al. (2007) methods that rely onconsumer self-reported attribute ideals or deviation from the idealdeliver more realistic ideal points than methods based on regres-sion-based techniques.

The present work proposed the application of a new penalty-based method on consumer responses to a CATA question to de-scribe the samples and their ideal product, as an extension to theapproach suggested by Plaehn (2012) when working with the emo-tional prole of drinks. Consumers are just asked to describe thesamples and their ideal product using a CATA question. Comparedto methodologies that rely on the use of scales, this approach

Table 10Summary of the recommendations for changes in the ve apple cultivars considered in Studquestion (Table 7), for the two consumer segments identied in Cluster analysis.

Cluster Sample Main recommended changes

1 Crisp pink Increase juiciness, sweetness and rmnessFuji Changes are not necessaryGrannysmith

Increase avorsome, sweetness and juiciness. Reduce sourne

Royal gala Increase taste intensity (to reduce deviation in avorsome, tReduce bitterness, softness, astringency and mealiness

Reddelicious

Reduce coarseness and mealiness. Increase taste intensity (tojuiciness, and sweetness

2 Crisp pink Increase juiciness, sweetness and taste intensity (to reduce dFuji Increase sweetness and taste intensity (to reduce deviation iGrannysmith

Reduce bitterness and sourness

Royal gala Increase juiciness and sweetness. Reduce softnessReddelicious

Changes are not necessary

Astringent 26 ns 13 30 ns 13 35Intercept 9.0 8.2 7.6 8.2Mean overall liking 7.7 6.3 7.4 6.1Mean drop* 1.3 1.9 0.2 2.1

: Indicates that the attribute was not included in the PLS model because less than 20% osignicant coefcients.* Mean drop is calculated as the intercept of the model minus the actual overall liking

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqr each of the attributes included in the CATA question, regression coefcients (RC) and

ny smith Royal gala Red delicious

ter 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2

RC % RC % RC % RC % RC % RC

ns 38 ns 82 0.10 50 ns 84 0.09 53 ns0.13 60 ns 55 0.15 50 0.25 65 0.14 38 ns0.14 78 ns 65 ns 55 0.25 59 0.09 23 0.36ns 28 0.33 26 0.09 10 26 ns 3 ns 33 ns 50 ns 40 ns 49 ns 30 ns0.15 63 0.30 48 ns 8 43 ns 10 ns 43 ns 73 0.09 45 ns 75 ns 33 ns0.16 58 ns 72 0.07 50 ns 70 0.08 55 nsns 10 37 ns 8 46 0.15 18 ns 18 55 0.13 48 0.21 60 ns 30 0.43ns 20 ns 38 0.08 25 ns 31 0.09 20 nsns 18 52 0.16 15 31 0.12 5 ns 10 53 0.09 33 ns 71 0.15 48 nsns 70 ns 65 ns 50 ns 58 0.11 43 ns

eference xxx (2013) xxxxxxwould be simpler and easier to use for consumers and could alsopotentially have a smaller impact on hedonic scores than JAR orintensity scales (Adams et al., 2007). Apart from its simplicity forconsumers, an advantage of the method is that it could be appliedwith a small set of products, not like regression-based methodsthat require larger sample sets. However, it must be consideredthat the number of samples should be 5 or more if factorial tech-niques such as CA or MFA are to be used for data analysis.

Asking consumers to describe their ideal product using a CATAquestion consists of a exible and simple add-onto a hedonic bal-lot. Its main advantages is that it provides information about con-sumer perception of the sensory characteristics of the products andalso information which enables to identify the sensory characteris-tics of consumer ideal product at the aggregate level and for con-sumer segments with different preference patterns. This enablesthe identication of drivers of liking for a set of products basedexclusively on consumer perception and without the need forregression techniques. In the studies included in the present arti-cle, the description of the ideal product provided by consumerswas similar to that of the samples with the highest liking scores,which indicates the validity of the information provided by

y 2, based on results from PLS modeling (Table 9) and consumer responses to the CATA

ss

asteless and odorless), juiciness, crispiness and rmness.

reduce deviation in apple avor, avorsome, tasteless and odorless), rmness,

eviation in avorsome). Reduce sournessn avorsome). Reduce crispiness

0.14 18 29 0.09 8 29 ns 3 8.4 5.2 8.8 8.6 7.8 8.86.4 4.2 5.2 8.2 5.2 6.72.0 1.0 3.6 0.4 2.6 2.1

f the consumers considered that it deviated from the ideal; ns: corresponds to non-

score.


d Prconsumers when describing their ideal product using CATA ques-tions. Similar results have been reported when asking consumersto describe their ideal product by rating attribute intensity usingscales in Ideal Prole Method (Worch et al., 2010, 2012a, 2012b).Further research is needed to investigate the stability of consumerdescriptions of their ideal product using a CATA question within asession and between sessions.

Penalty analysis based on the comparison of consumer percep-tion of the samples and their ideal product provided informationabout the impact of deviation from the ideal on liking scores, di-rectly from consumers. A graphical representation of the relation-ships between overall liking scores and deviation from the idealproduct was obtained, as well as the potential improvement inoverall liking scores and information about the impact of deviationfrom the ideal of each attribute. Meanwhile, the direction of thesensory changes needed to reduce the deviation from the idealwas obtained from the difference between the percentage of con-sumers who used a term for describing the samples and the idealproduct. This type of analysis enabled to make specic and action-able recommendations for each product based on the inuence ofdeviation from the ideal on overall liking. Besides, PLS modelingprovided information about the potential for improvement foreach product, enabling a realistic decision as to the value of refor-mulation. The main disadvantage of the method is related to thefact that information about attribute intensity and the degree ofdifference between the products and the ideal for each consumeris not gathered.

In the present study differences in the inuence of deviationfrom the ideal when the product is less or more intense in eachspecic attribute were not considered. However, the PLS dummyapproach could be easily performed by considering two differentdummy variables for each attribute, one which indicates if theattribute is used to describe the product and not the ideal, and asecond one which indicates if the attribute is used to describethe ideal and not the product. A similar approach has been usedby Xiong and Meullenet (2006) when dealing with JAR scales.

Another drawback could be how the terms included in the CATAquestion were selected, if not chosen appropriately some drivers ofliking or disliking might be missed, but this fact is inherent to allattribute-based descriptive techniques.

Further research and comparison with other optimization tech-niques, such as ideal prole and JAR scales, would be needed. Apartfrom comparing ideal products and recommendations for productreformulation, it would be necessary to compare the methodolo-gies in terms of ease of use and time required for completing thetask. Besides, it would also be necessary to study the minimumnumber of consumers needed for obtaining reliable product spacesfrom CATA questions. Considering that the proposed penalty-basedapproach relies on overall liking scores, working with the usualnumber of consumers considered in hedonic tests (100120)(Hough et al., 2006) seems reasonable for obtaining a reliable iden-tication of drivers of liking and directions for product reformula-tion. However, the number of consumers to be included in thestudy also depends on the number of segments that are soughtto be identied. Due to the methodological nature of the presentwork only 74 consumers were considered for Study 1, which doesnot compromise its validity.

Another interesting issue that arose from the results is relatedto differences between consumer segments in the way they de-scribe the evaluated products. Consumers tended to associate odorand avor terms, such as Apple odor and avor, to their preferredsamples, indicating that their evaluation of these terms werestrongly affected by their preference patterns. Ares et al. (2010)

G. Ares et al. / Food Quality anand Lado, Vicente, Manzzioni, and Ares (2010) also reported thatconsumer segments with different preference patterns differed inthe way in which they used some terms of a CATA question to de-

Please cite this article in press as: Ares, G., et al. Penalty analysis based on CAmulation. Food Quality and Preference (2013), http://dx.doi.org/10.1016/j.foodqscribe samples. In particular, Lado et al. (2010) found that the maindifferences between consumer segments were observed in theterms related to total odor and avor intensity. The fact of ndingdifferences in the use of sensory terms depending on the prefer-ence pattern is indeed worth of further investigation. Is it that pref-erence in a way biases the description? Is it that consumersidealize the sensory characters of the perfect product in theirminds? What kind of attributes would be affected by this? Or isit simply a matter of attribute denition? From the present study,and also from Lado et al. (2010), it seems that mainly attributesless dened and that describe typicality might be the ones moreaffected. Attributes like rm, crispy, soft, mealy or sweet have beensimilarly used by both clusters in this study, while other attributeslike apple avor where the ones that differed. For a consumer,the apple avor in their preferred or ideal apple might be a par-ticular avor that they would rate more intensely or tick more fre-quently in a CATA, even if another sample has a more intense avorbut not corresponding with prole they have in their minds ashow an apple should taste.

This result coming from this work suggests the need for furtherresearch related to the selection of terms to be included in a CATAquestion and particularly to study the validity of consumer evalu-ations of complex sensory attributes. On the other hand, the factthat consumers responses to a CATA question might be inuencedby their preference patterns makes the inclusion of informationabout the ideal product interesting for better understanding con-sumer perception of the sensory characteristics of a set of productsand for the identication of their drivers of liking.

Acknowledgements

The authors are indebted to Comisin Sectorial de InvestigacinCientca (CSIC) from Universidad de la Repblica for nancial sup-port and to Comisin Administradora del Mercado Modelo for pro-viding the apple samples used in Study 1. Also, the authors aregrateful to the Spanish Ministry of Science and for the contractawarded to the author P. Varela (Juan de la Cierva Program). Theauthors would like to thank Luca Antnez, Alejandra Sapolinskiand Leticia Vidal for their help with data collection in Study 1.

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Penalty analysis based on CATA questions to identify drivers of liking and directions for product reformulation1 Introduction2 Materials and methods2.1 Study 1: yogurt study2.1.1 Samples2.1.2 Consumer testing

2.2 Study 2: apple study2.2.1 Samples2.2.2 Consumer testing

2.3 Data analysis

3 Results3.1 Study 1: yogurt samples3.1.1 Texture liking scores3.1.2 CATA counts3.1.3 Penalty analysis

3.2 Study 2: apple samples3.2.1 Overall liking scores3.2.2 CATA counts3.2.3 Penalty analysis

4 Discussion and conclusionsAcknowledgementsReferences

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Analisis de Penalidades Cata (1)