Penalty analysis based on CATA questions to identify Penalty analysis based on CATA questions to identify drivers of liking and directions for product drivers of liking and directions for product

reformulationreformulation

Gastón AresGastón Ares11, Cecilia Dauber, Cecilia Dauber11, Elisa Fernández, Elisa Fernández11, Ana Giménez, Ana Giménez11, , Paula VarelaPaula Varela22

1 Facultad de Química. Universidad de la República. Montevideo, Uruguay

2 Instituto de Agroquímica y Tecnología de Alimentos, Valencia, Spain.Instituto de Agroquímica y Tecnología de Alimentos, Valencia, Spain.

1111th Sensometrics, 10-13 July 2012, Rennes,

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IntroductionIntroduction

DuringDuring newnew productproduct developmentdevelopment oneone ofof thethe challengeschallenges forfor

IntroductionIntroduction

DuringDuring newnew productproduct developmentdevelopment,, oneone ofof thethe challengeschallenges forforSensorySensory && ConsumerConsumer ScienceScience isis toto provideprovide actionableactionableinformationinformation forfor specificspecific changeschanges inin productproduct formulationformulation((MoskowitzMoskowitz && HartmannHartmann 20082008))((MoskowitzMoskowitz && HartmannHartmann,, 20082008))..

ManyMany strategiesstrategies havehave beenbeen usedused inin productproduct optimizationoptimization forforidentifyingidentifying driversdrivers ofof likingliking andand idealideal productsproducts::identifyingidentifying driversdrivers ofof likingliking andand idealideal productsproducts::

PreferencePreference mappingmapping basedbased onon sensorysensorycharacterizationcharacterization ofof thethe productsproducts (van(van KleefKleef etet alal..,, 20062006))..characterizationcharacterization ofof thethe productsproducts (van(van KleefKleef etet alal..,, 20062006))..

ConsumerConsumer--basedbased sensorysensory characterizationscharacterizations ((DooleyDooley etet alal..,,

20102010;; AresAres etet alal..,, 20102010;; VarelaVarela && Ares,Ares, 20122012))..))

ConsumersConsumers’’ descriptiondescription ofof thethe idealideal productproduct

11th Sensometrics, 10-13 July 2012, Rennes, France 2

JustJust--aboutabout--rightright scalesscales (JAR)(JAR)

ConsumersConsumers evaluateevaluate aa setset ofof attributesattributes asas deviationsdeviations fromfromthethe idealideal ((LawlessLawless && HeymannHeymann,, 20102010))..(( yy ,, ))

SimpleSimple andand commoncommon approachapproach

PenaltyPenalty analysisanalysis enablesenables thethe identificationidentification ofof directionsdirections forforproductproduct reformulationreformulation ((XiongXiong && MeullenetMeullenet,, 20062006))..

TheyThey havehave raisedraised severalseveral concernsconcerns regardingregarding theirtheirinfluenceinfluence onon overalloverall likingliking scoresscores ((EplerEpler etet alal..,,19981998;; PopperPopper etet alal..,,influenceinfluence onon overalloverall likingliking scoresscores ((EplerEpler etet alal..,,19981998;; PopperPopper etet alal..,,

20042004))..

11th Sensometrics, 10-13 July 2012, Rennes, France 3

IdealIdeal profileprofile methodmethod

ConsumersConsumers raterate thethe intensityintensity ofof aa setset ofof attributesattributes forfor thetheConsumersConsumers raterate thethe intensityintensity ofof aa setset ofof attributesattributes forfor thethesamplessamples andand theirtheir idealideal productproduct usingusing scalesscales ((WorchWorch etet alal..,,

20102010;; WorchWorch etet alal..,, 20122012a,a, 20122012b)b)..

IdealIdeal productproduct descriptionsdescriptions areare similarsimilar toto thethe mostmost likedlikedproductsproducts..pp

ProvidesProvides actionableactionable informationinformation forfor productproduct reformulationreformulation..

11th Sensometrics, 10-13 July 2012, Rennes, France 4

CheckCheck--allall--thatthat--applyapply (CATA)(CATA) questionsquestions

HaveHave gainedgained popularitypopularity forfor sensorysensory characterizationcharacterization ofofHaveHave gainedgained popularitypopularity forfor sensorysensory characterizationcharacterization ofoffoodfood productsproducts withwith consumersconsumers (Adams(Adams etet alal..,, 20072007;; DooleyDooley etet alal..,,

20102010;; AresAres etet alal..,, 20102010;; AresAres etet alal..,, 20112011))..

ConsumersConsumers areare presentedpresented aa listlist ofof termsterms andand areare askedasked totocheckcheck allall thethe termsterms theythey considerconsider appropriateappropriate toto describedescribeaa samplesampleaa samplesample..

Quick,Quick, simplesimple andand easyeasy tasktask forfor consumersconsumers (Adams(Adams etet alal..,,20072007))..

ItIt hashas beenbeen usedused toto describedescribe consumersconsumers’’ idealideal productproduct((CowdenCowden etet alal..,, 20092009;; AresAres etet alal..,, 20112011))..

Penalty/rewardPenalty/reward analysisanalysis forfor emotionalemotional termsterms ((PlaehnPlaehn,, 20122012))..

11th Sensometrics, 10-13 July 2012, Rennes, France 5

AimAim ofof thethe studystudyAimAim of of thethe studystudy

ApplyApply penaltypenalty analysisanalysis basedbased onon consumerconsumerresponsesresponses toto aa CATACATA questionquestion aboutabout aa setset ofofsamplessamples andand theirtheir idealideal productproduct toto identifyidentify driversdriverssamplessamples andand theirtheir idealideal productproduct toto identifyidentify driversdriversofof likingliking andand directionsdirections forfor productproduct reformulationreformulation..

6611th Sensometrics, 10-13 July 2012, Rennes,

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MaterialsMaterials andand methodsmethodsMaterialsMaterials and and methodsmethods

StudyStudy 11:: YogurtsYogurtsyy gg

oo 7474 consumersconsumers evaluatedevaluated 88 yogurtsyogurts formulatedformulatedfollowingfollowing aa 2233 fullfull factorialfactorial designdesign forfor fatfat contentcontent,,gg gggelatingelatin andand starchstarch..

oo TheyThey triedtried thethe yogurts,yogurts, ratedrated theirtheir texturetexture likingliking usingusingaa 99--pointpoint hedonichedonic scalescale andand answeredanswered aa CATACATAquestionquestion composedcomposed ofof 1616 texturetexture termsterms

ThTh ll dd thth CATACATA titi ff th ith i id lid loo TheyThey alsoalso answeredanswered thethe CATACATA questionquestion forfor theirtheir idealidealyogurtyogurt..

Smooth Viscous Homogeneous LiquidSmooth Viscous Homogeneous Liquid

Lumpy Creamy Sticky Rough

Gummy Thick Gelatinous Firm

7711th Sensometrics, 10-13 July 2012, Rennes,

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Heterogeneous Consistent Runny Mouth-coating

StudyStudy 22:: ApplesApplesyy pppp

oo 119119 consumersconsumers evaluatedevaluated 55 commercialcommercial appleapplecultivarscultivars..

oo TheyThey triedtried thethe applesapples,, ratedrated theirtheir overalloverall likingliking usingusing aa99--pointpoint hedonichedonic scalescale andand answeredanswered aa CATACATA questionquestioncomposedcomposed ofof 1515 odourodour,, flavourflavour andand texturetexture termsterms

oo TheyThey alsoalso answeredanswered thethe CATACATA questionquestion forfor theirtheir idealidealllappleapple..

Firm Sour Odourless Juicy CrispyFirm Sour Odourless Juicy Crispy

Tasteless Sweet Flavoursome Mealy Bitter

Coarse Apple flavour Apple odour Soft Astringent

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DataData analysisanalysisyy

oo OverallOverall likingliking scoresscores•• ANOVAANOVAANOVAANOVA•• ClusterCluster analysisanalysis onon datadata fromfrom StudyStudy 22

CATACATA iioo CATACATA questionquestion•• FrequencyFrequency ofof useuse•• Cochran’sCochran’s QQ testtest•• Cochran sCochran s QQ testtest•• CorrespondenceCorrespondence analysisanalysis

oo PenaltyPenalty analysisanalysis

11th Sensometrics, 10-13 July 2012, Rennes, France 9

oo PenaltyPenalty analysisanalysis

•• DummyDummy variablevariable approachapproach

Consumer Sample Firm Sour Odourless Juicy AstringentConsumer Sample Firm Sour Odourless Juicy … Astringent

1 Crisp Pink 0 1 0 1 … 0

1 … … … … … … …0 i di t th t th tt ib t d t… … … … … … … …

119 Royal gala … … … … … …

0: indicates that the attribute was used todescribe the sample as in the ideal product

1: indicates that the attribute was used differently

11th Sensometrics, 10-13 July 2012, Rennes, France 10

yto describe the sample and the ideal product

oo PenaltyPenalty analysisanalysis

•• TheThe percentagepercentage ofof consumersconsumers whowho usedused anan attributeattributedifferentlydifferently forfor describingdescribing eacheach samplesample andand thethe idealidealproductproduct

ThresholdThreshold:: 2020%% ((XiongXiong && MeullenetMeullenet,, 20062006;; PlaehnPlaehn,, 20122012))..

•• MeanMean dropdrop associatedassociated withwith thethe deviationdeviation fromfrom thetheidealideal..

KruskalKruskal--WallisWallis testtestKruskalKruskal WallisWallis testtest

•• PartialPartial--leastleast squaressquares (PLS)(PLS) regressionregressionOverallOverall likingliking asas dependentdependent variablevariable andand dummydummyOverallOverall likingliking asas dependentdependent variablevariable andand dummydummyvariablesvariables asas regressorsregressors ((XiongXiong && MeullenetMeullenet,, 20062006))..

11th Sensometrics, 10-13 July 2012, Rennes, France 11

ResultsResultsResultsResults

StudyStudy 11:: YogurtsYogurts

TextureTexture likingliking scoresscores

7

5.6 a5.2 a,b

5.6 a5.9 a

5.3 a,b

7

4.2 c,d

3.5 d

4.4 b,c5

king

 (1‐9)

3

Texture li

11 2 3 4 5 6 7 8

Samples

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Samples

FrequencyFrequency ofof useuse ofof thethe termsterms ((%%))

Attribute Sample1 2 3 4 5 6 7 8 Ideal

Smooth *** 41 53 12 38 62 64 23 45 92Lumpy *** 32 7 57 11 26 11 61 8 1Viscous ns 5 8 18 7 14 12 7 15 12Homogeneous *** 20 39 8 49 26 57 5 43 80

i id *** 3 4 23 3 4 1 22 0 3

Smoothness, Homogeneityand Creaminess main driversLiquid *** 73 4 23 3 45 1 22 0 3

Thick *** 3 32 23 49 8 43 30 51 38Gelatinous *** 1 30 4 31 0 22 0 26 0Firm *** 0 36 1 47 1 45 8 65 20

and Creaminess main driversof texture liking, in agreementwith previous studies(Pohjanheimo & Sandell, 2009;Firm 0 36 1 47 1 45 8 65 20

Sticky * 3 4 14 3 3 4 8 8 0Creamy ** 16 35 18 36 35 38 32 38 86Rough *** 24 5 46 16 9 7 46 11 0

( o ja e o & Sa de , 009;Bayarri et al., 2011).

Consistent *** 0 45 9 57 11 45 20 55 31Mouth-coating * 15 11 30 16 14 19 24 16 9Gummy ns 1 0 4 5 1 1 7 5 0Runny *** 55 11 20 3 47 5 15 0 18

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Runny 55 11 20 3 47 5 15 0 18Heterogenous *** 32 19 49 4 18 7 42 0 3

The ideal yogurt was closeto the samples with thehighest texture liking scoreshighest texture liking scoresand far from the leastpreferred samples.

11th Sensometrics, 10-13 July 2012, Rennes, France 14

PenaltyPenalty analysisanalysis

3

Sample 1

2scores Thick, Homogeneous and

Liquid were the most

Lumpy Homogeneous

Thick

Consistent

2exture liking

 s relevant attributes.

g

Liquid

Sticky

R h

Mouth‐coating

GummyRunny

Heterogeneous

1

ean drop

 inte

Smooth

ViscousGelatinous

RoughMe

Creamy0

0 10 20 30 40 50 60 70 80 90

Percentage of consumers (%)

11th Sensometrics, 10-13 July 2012, Rennes, France 15

Recommended changes:Increase in Homogeneity and

Att ib t Sample

Increase in Homogeneity andThickness

Attribute p1 2 3 4 5 6 7 8 Ideal

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

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g

3

Sample 6

Rough

2scores

The percentage of

SmoothLumpy

Viscous

HomogeneousLiquid

GummyHeterogeneous

2exture liking

 s The percentage ofconsumers whoconsidered that theattributes deviated from

ThickSticky

CreamyConsistent

1

ean drop

 in te the ideal was lower than

50%. Smooth, Creamy,and Consistent were the

Thick

Gelatinous

Firm

Mouth‐coating

Runny

Me

most relevant attributes.

Runny

00 10 20 30 40 50 60 70 80 90

Percentage of consumers (%)

11th Sensometrics, 10-13 July 2012, Rennes, France 17

Recommended changes: anincrease in smoothnees, and

i d d i

Att ib t Sample

creaminess, and a decrease inconsistency.

Attribute p1 2 3 4 5 6 7 8 Ideal

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

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g

RegressionRegression coefficientscoefficients fromfrom PLSPLS modelmodel

TSample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8

RegressionRegression coefficientscoefficients fromfrom PLSPLS modelmodel

TermSample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8% RC % RC % RC % RC % RC % RC % RC % RC

Smooth 62 -0.15 50 -0.24 82 -0.17 59 -0.21 41 -0.16 41 -0.20 77 -0.14 53 -0.14Lumpy 31 -0.31 8 - 55 -0.10 12 - 27 -0.15 12 - 59 ns 9 -Viscous 18 - 12 - 16 - 14 - 20 ns 14 - 16 - 22 -0.15Homogeneous 65 -0.13 49 -0.18 77 -0.08 39 -0.17 59 ns 28 -0.16 74 -0.10 36 nsLiquid 73 -0.14 4 - 26 -0.09 5 - 45 -0.18 4 - 24 ns 3 -Thick 38 ns 32 ns 34 ns 46 ns 35 ns 41 ns 32 ns 43 nsGelatinous 1 - 30 ns 4 - 31 ns 0 - 22 ns 0 - 26 nsFirm 20 ns 41 ns 22 ns 41 ns 19 - 46 ns 26 -0.14 55 -0.15Sticky 3 - 4 - 14 ns 3 - 3 - 4 - 8 ns 8 -Creamy 73 ns 57 -0.18 69 -0.10 58 -0.32 59 ns 51 -0.16 57 -0.19 57 -0.35Rough 24 -0.17 5 - 46 -0.09 16 - 9 - 7 - 46 -0.14 11 -Consistent 41 ns 45 ns 39 ns 41 ns 38 ns 39 -0.17 39 ns 45 -0.18Mouth-coating 22 -0.13 12 - 34 -0.10 20 ns 18 - 15 - 28 -0.11 18 -Gummy 1 - 0 - 4 - 5 - 1 - 1 - 7 - 5 -Runny 51 ns 23 ns 30 -0.09 18 - 35 -0.13 23 ns 27 -0.11 18 -Heterogenous 35 -0.15 22 ns 49 -0.12 7 - 18 - 9 - 45 -0.20 3 -Intercept 7.2 7.2 6.3 7.0 6.9 7.3 7.3 7.4Mean drop (*) 3.0 1.8 2.8 1.8 1.0 1.4 2.9 2.1

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StudyStudy 22

FrequencyFrequency ofof useuse ofof thethe termsterms ((%%)) forfor thethe wholewhole consumerconsumersamplesample

yy

pp

AttributeSample

Crisp pink Fuji Granny smith Royal gala Red delicious IdealFirm *** 68 70 66 19 18 79Juicy *** 63 76 49 51 48 92Sweet *** 32 39 5 31 61 77Bitt *** 5 10 18 6 3 2Bitter *** 5 10 18 6 3 2

Apple odour *** 13 8 8 5 8 39Sour *** 52 12 80 7 3 22

Crispy *** 66 55 46 16 11 64

Firmness, Juiciness, Sweetness,Crispiness and Apple flavour werethe main drivers of liking.py

Flavoursome *** 43 44 25 25 31 76Coarse *** 3 1 2 15 24 3

Soft *** 1 2 2 49 45 6Od l *** 13 14 14 22 14 1Odourless *** 13 14 14 22 14 1Tasteless *** 4 9 8 31 10 0

Mealy *** 1 0 1 36 58 5Apple flavour *** 45 40 14 25 37 69

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ppAstringent *** 8 7 16 3 1 7

OverallOverall likingliking scoresscores

8.2c9

6.4 b

7.7 c7.4c

6.3 b6.7b

6.1b7

res (1‐9)

5.2a  5.2a

4.2 a5

liking scor

Cluster 1 (n=79)

Cluster 2 (n=40)

3

Overall  Cluster 2 (n 40)

Cluster 1 preferred Crisp Pink andFuji apples, whereas Cluster 2preferred Red Delicious apples

1Granny Smith

Crisp Pink Royal gala Fuji Red Delicious

preferred Red Delicious apples

11th Sensometrics, 10-13 July 2012, Rennes, France 21

Sour

1

Cluster 1 (n=79)

Astringent

Sour

G ith

Odourless

SoftMealyTasteless Coarse

BitterGranny smith

Royal galaRed delicious014

.8%)

Apple flavour

Firm Apple odour

FlavoursomeJuicy

Sweet

CrispyCrisp pink

0‐1 0 1 2

Dim 2 (

The ideal apple waslocated close to CrispApple flavourFujiIdeal located close to CrispPink and Fuji apples.Firmness, Crispinessand Apple flavour were

‐1

Dim 1 (76 8%)

and Apple flavour werethe main drivers of liking.

11th Sensometrics, 10-13 July 2012, Rennes, France 22

Dim 1 (76.8%)

1

Cluster 1 (n=79)

Soft

SourC

Odourless

Mealy

Apple odourAstringent

TastelessCoarse

Bitter Granny smith Royal gala

04.6%

)

Apple flavourFirm

Flavoursome

JuicySweet

CrispyCrisp pink

Fuji

Red delicious0

‐1 0 1 2

Dim 2 (14

The ideal apple was locatedFuji

Ideal close to Red delicious andFuji apples.Sweetness and Apple

‐1Dim 1 (75.1%)

flavour were the maindrivers of liking.

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8992 93

100

89

76 758080

68

80

ers (%

)

41

60

3843

40

60

age of con

sume

Cluster 1 (n=79)

Cluster 2 (n=40)

29

38

1820

40

Percen

ta

1 1 03

85

0Firm Juicy Sweet Bitter Apple odour Sour Crispy Flavoursome Coarse Soft

The clusters differred in their description of the ideal apple,particularly in the frequency of mention of the terms Firm,Sour Crispy and Soft

11th Sensometrics, 10-13 July 2012, Rennes, France 24

Sour, Crispy and Soft

PenaltyPenalty analysisanalysis atat thethe aggregateaggregate levellevel

Cluster 1: Tasteless, Coarse, Soft,M l J i Fi FlMealy, Juicy, Firm, Flavoursome

Cluster 2: Tasteless, Sweet, Bitter,J ic So r TastelessJuicy, Sour, Tasteless

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RegressionRegression coefficientscoefficients fromfrom PLSPLS modelmodel

TermCrisp pink Fuji Red delicious

Cluster 1 Cluster 2 Cluster 1 Cluster 2 Cluster 1 Cluster 2% RC % RC % RC % RC % RC % RC

Firm 42 -0.13 35 ns 38 ns 30 ns 84 -0.09 53 ns

Juicy 45 -0.31 53 -0.23 37 -0.16 35 ns 65 -0.14 38 ns

Sweet 59 -0.16 70 -0.23 50 -0.13 70 -0.19 59 -0.09 23 -0.36

Bitter 23 ns 10 - 27 -0.18 10 - 26 ns 3 -

Apple odour 47 ns 40 ns 48 ns 33 ns 49 ns 30 ns

Sour 49 ns 65 -0.17 43 ns 15 - 43 ns 10 -

Crispy 36 ns 40 ns 49 -0.13 40 -0.19 75 ns 33 ns

Flavoursome 49 ns 53 -0.14 54 ns 58 -0.22 70 -0.08 55 ns

Coarse 23 ns 5 - 23 ns 5 - 46 -0.15 18 -

Soft 22 ns 18 ns 23 ns 18 60 ns 30 0 43Soft 22 ns 18 ns 23 ns 18 - 60 ns 30 -0.43

Odourless 30 ns 20 ns 30 ns 18 - 31 -0.09 20 ns

Tasteless 22 ns 10 - 27 -0.29 13 - 31 -0.12 5 -

Mealy 24 ns 10 - 24 ns 8 - 71 -0.15 48 ns

Apple flavour 48 ns 55 ns 51 -0.11 50 ns 58 -0.11 43 ns

Astringent 26 ns 13 - 30 ns 13 - 29 ns 3 -

Intercept 9 0 8 2 7 6 8 2 7 8 8 8

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Intercept 9.0 8.2 7.6 8.2 7.8 8.8Mean drop 1.3 1.9 0.2 2.1 2.6 0.6

DiscussionDiscussion andand ConclusionsConclusionsDiscussionDiscussion and and ConclusionsConclusions

ThTh th d lth d l blbl tt id tifid tif ththTheThe methodologymethodology waswas ableable toto identifyidentify thethe sensorysensorycharacteristicscharacteristics ofof thethe idealideal productproduct,, whichwhich werewere similarsimilar totothosethose ofof thethe mostmost likedliked productsproducts..

SimpleSimple andand flexibleflexible addadd--onon toto usualusual CATACATA ballotsballots..

ProvidesProvides informationinformation forfor thethe identificationidentification ofof driversdrivers ofof likingliking,,eveneven forfor consumersconsumers withwith differentdifferent preferencepreference patternspatterns,, andandrecommendationsrecommendations forfor productproduct reformulationreformulation..

DoesDoes notnot provideprovide aa measuremeasure ofof thethe degreedegree ofof differencedifferenceDoesDoes notnot provideprovide aa measuremeasure ofof thethe degreedegree ofof differencedifferencebetweenbetween thethe productproduct andand thethe idealideal..

11th Sensometrics, 10-13 July 2012, Rennes, France 27

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Varela,Varela, PP..,, && Ares,Ares, GG.. ((20122012)).. SensorySensory profiling,profiling, thethe blurredblurred lineline betweenbetween sensorysensory andand consumerconsumer sciencescience..AA reviewreview ofof novelnovel methodsmethods forfor productproduct characterizationcharacterization.. FoodFood ResearchResearch International,International, InIn presspress..

WorchWorch,, TT..,, Dooley,Dooley, LL..,, MeullenetMeullenet,, JJ..FF..,, Punter,Punter, PP..HH.. ((20102010)).. ComparisonComparison ofof PLSPLS dummydummy variablesvariables andandFishborneFishborne methodmethod toto determinedetermine optimaloptimal productproduct characteristicscharacteristics fromfrom idealideal profilesprofiles.. FoodFood QualityQuality andandFishborneFishborne methodmethod toto determinedetermine optimaloptimal productproduct characteristicscharacteristics fromfrom idealideal profilesprofiles.. FoodFood QualityQuality andandPreference,Preference, 2121,, 10771077--10871087..

WorchWorch,, TT..,, LêLê,, SS..,, Punter,Punter, PP..,, && PagèsPagès,, JJ.. ((20122012a)a).. ExtensionExtension ofof thethe consistencyconsistency ofof thethe datadata obtainedobtained bybythethe IdealIdeal ProfileProfile MethodMethod:: WouldWould thethe idealideal productsproducts bebe moremore likedliked thanthan thethe testedtested products?products? FoodFoodQualityQuality andand Preference,Preference, 2626,, 7474--8080..yy ,, ,,

WorchWorch,, TT..,, LêLê,, SS..,, Punter,Punter, PP..,, && PagèsPagès,, JJ.. ((20122012b)b).. AssessmentAssessment ofof thethe consistencyconsistency ofof idealideal profilesprofilesaccordingaccording toto nonnon--idealideal datadata forfor IPMIPM.. FoodFood QualityQuality andand Preference,Preference, 2424,, 9999--110110..

XiongXiong,, RR..,, && MeullenetMeullenet,, JJ.. FF.. ((20062006)).. AA PLSPLS dummydummy variablevariable approachapproach toto assessassess thethe impactimpact ofof JARJARattributesattributes onon likingliking.. FoodFood QualityQuality andand Preference,Preference, 1717,, 188188––198198..

11th Sensometrics, 10-13 July 2012, Rennes, France 29

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ThankThank youyou veryvery muchmuchforfor youryour kindkind attentionattention!!

Facultad de Química Universidad de la República Montevideo Uruguay

Gastón AresGastón Ares

Facultad de Química. Universidad de la República. Montevideo, UruguayEmail: gares@fq.edu.uy

3011th Sensometrics, 10-13 July 2012, Rennes,

France