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Judging wine quality: Do we need experts, consumers or trainedpanelists?

Helene Hopfer , Hildegarde HeymannDepartment of Viticulture & Enology, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA

a r t i c l e i n f o

Article history:Received 12 July 2013Received in revised form 27 September 2013Accepted 8 October 2013Available online 17 October 2013

Keywords:Wine qualityCalifornian Cabernet SauvignonDescriptive AnalysisHedonic likingConsumersWine experts

a b s t r a c t

A Descriptive Analysis panel, wine experts and consumers evaluated 27 Californian Cabernet Sauvignonwines with varying quality scores. Descriptive Analysis revealed several aroma and avor descriptorsdriving quality scores. For all consumer segments as well as the wine experts, hedonic liking was shownto highly correlate to perceived quality, but for some consumers liking and perceived quality was not atall correlated to the quality scores of the wines. Wine experts were able to nd signicant differences inliking and quality, but did not agree completely with the assigned quality scores from the wine judgment.Wine experts also used a combination of both descriptive and hedonic terms when describing a highquality wine, indicating that they are better at communicating and describing what they like.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

The quality of wine is hard to dene, mainly due to the lack of agreement on the quality term in general, and this discussion is notlimited to wine alone. People who study wine quality therefore talkabout perceived quality, and how various populations differ in theirwine quality perception ( Charters & Pettigrew, 2007 ). The advantageof using a holistic approach, e.g. quality perception, lies in the globalassessment of quality, which is the result of individuals conceptionsandprevious experiences, andincorporates alldifferentlevels of qual-ity into one judgment ( Charters & Pettigrew, 2007 ). Nevertheless, theoverall quality perception can be broken into several dimensions of extrinsicand intrinsiclayers ( Charters& Pettigrew,2007;Verd Jover,Llorns Montes, & Fuentes Fuentes, 2004 ). Extrinsic factors includegrape growing and winemaking, and, at a lower level, the technicalcorrectness including the most basic denition of wine quality asthe absence of faults and/or drinkability. The intrinsic dimension ismore dened by the drinking experience, including factors such aspleasure, aroma, avor and mouthfeel, appearance, as well as factorsthat aretypically more importantfor people with a high involvementsuch as origin, variety, typicality and potential.

When talking about the different dimensions of quality, oneneeds to keep in mind that the two levels inuence each other,as shown by Siegrist and Cousin (2009) , who found that extrinsic

information, such as wine critic scores, directly inuence theexpectation and therefore, also the tasting experience. Similarly,consumers found signicant differences in liking of Champagnewines when they were able to see the labels, but in contrast, couldnot differentiate among the same Champagne wines when tastedblindly ( Lange, Martin, Chabanet, Combris, & Issanchou, 2002 ).

Consumers are inuenced by extrinsic information, however,they report that the intrinsic tasting experience is the mostimportant reason for drinking wine ( Charters & Pettigrew,2007 ), indicating the importance of avor, i.e. as dened bythe ASTM as the . . . perception resulting from stimulating a com-bination of the taste buds, the olfactory organs, and chemesthetic receptors within the oral cavity . . . (ASTM International, 2009 ).In the end, consumers of wine want to drink and enjoy qualitywine, a fact, that is true for everyone independent of the degreeof wine involvement ( Charters & Pettigrew, 2007 ). This also indi-cates that perceived quality is linked to hedonic liking ( Lawless,Liu, & Goldwyn, 1997 ). However, the average consumers, espe-cially those with a lower degree of wine involvement, do notnecessarily have the tasting experience and expertise to selectappropriate wines, and so turn towards wine experts and trustedsources for guidance, followed by brand, awarded medals andwine articles. They also tend not to use back and front labelsor store display information in their decision making process(Thach, 2008 ).

Ideally, wine experts screen wines and award some kind of quality score, which would then give consumers an indicationwhether they would enjoy and like a wine or not.

0950-3293/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.foodqual.2013.10.004

Corresponding author. Tel.: +1 530 752 9356; fax: +1 530 752 0382.E-mail addresses: [email protected] (H. Hopfer), [email protected]

(H. Heymann).

Food Quality and Preference 32 (2014) 221233

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Experts are known to act more analytically when assessingquality compared to inexperienced consumers ( DAlessandro andPecotich, 2013 ). However, as with every product, levels of likingand also perceived quality show large variabilities, not only amongconsumers, but also among wine experts ( Hodgson, 2008, 2009 ).Hodgson (2009) calculated that being awarded a Gold Medal inone of the many wine judgements is simply a matter of how many

competitions you enter, as he could not nd concordance in goldmedals awarded among the 13 U.S. wine competitions studied.These factors and previous studies on perceived wine quality,

using either experts or consumers, set the stage for our study,where we evaluated a set of wines, varying in quality, with threedifferent populations wine experts, trained panelists and con-sumers, in an attempt to gain a broader understanding of perceivedwine quality in a set of commercial Cabernet Sauvignon winesfrom California.

2. Materials and methods

The study was approved by the UC Davis Institutional ReviewBoard (IRB, protocol number 305379-2).

2.1. Samples

Twenty-seven Cabernet Sauvignon wines from 9 Californianwine regions were selected for the study based on their perfor-mance in the 2012 California State Fair Commercial Wine Compe-tition. Any bonded winery can enter their grape or fruit product

grown in California in the competition. The entered wine mustbe from a lot of at least 300 gal (i.e. 1135.62 L), and at least240 gal (i.e. 908.50 L) of this lot must be available for sale(http://www.bigfun.org/wp-content/uploads/2012/02/2012-Com-Wine-Pros-4pages.pdf ).

A total of 333 Cabernet Sauvignon wines were entered in thecompetition in 2012, coming from 9 wine regions in California,

which are geographically designated and established by the ofciallegal body, the Alcohol and Tobacco Tax and Trade Bureau (TTB).From each region three wines were selected, one considered highin quality (i.e. the highest scoring wine, in most cases either a Goldor a Double Gold wine, except for region H where the highest scor-ing wine was a Silver medal (W27)), one low in quality (i.e. a Noaward wine, scoring lowest in the region), and one wine of mediumquality (around the average point score between the high and thelow quality wine). For 7 out of the 9 regions wines from all threequality categories could be acquired, with the exception of regionH (no Gold or Double Gold available) and region G (no No awardwine available). From region H we had two No award wines (W5and W21), one Bronze wine (W7) and one Silver wine (W27). Winevintages varied between 2001 and 2011 (median=2009), and retailprices varied between $9.99 and $70.00 per bottle with a medianprice of $26.95 ( Table 1 ).

2.2. Descriptive Analysis (DA) panel

All wines were characterized by a generic Descriptive Analysis(DA) (Lawless, 2010 ), using a panel of 15 trained judges (10 males;

Table 1

Wines used in the study together with their information (code, region, awarded points and medals in the wine competition, bottle retail price) and average hedonic liking (HL)and quality (Q) scores for the consumers (cons) and experts (exp). Letters denote signicant differences in HL and Q using 1-way ANOVA and post hoc analysis according to Tukey(P 6 0.05). Columns that share the same letter are not signicantly different from each other ( P 6 0.05).

Code Vintage Region a Pts. Awards b Retail ($)price HLcons Qcons HLexp

W1 2008 G 82 NA 26.95 4.10 e 4.56 abc 2.89 eW2 2009 B 89 S 39.00 4.60 abcde 4.99 a 3.54 cde

W3 2009 I 95 G 21.00 4.86 abc 4.91 a 5.04 abcdW4 2008 G 90 S 34.00 4.87 abc 4.53 abc 4.25 abcdeW5 c 2006 H 83 NA 15.00 4.90 abc 4.34 abc 2.68 e

3.68 cdeW6 2009 C 90 S 55.00 4.97 abc 4.89 a 5.30 abcW7 2010 H 86 B 25.00 4.98 ab 4.88 a 5.00 abcdW8 2008 C 98 DG 47.00 5.04 ab 4.63 abc 5.32 abcW9 2009 D 94 G 25.00 5.24 a 4.65 abc 5.32 abcW10 2009 A 94 G 9.99 4.10 e 4.68 abc 5.11 abcdW11 2007 A 82 G 38.00 4.11 de 4.39 abc 3.86 bcdeW12 c 2009 F 89 S 15.00 4.24 cde 4.70 abc 5.00 abcd

5.25 abcW13 2007 D 88 S 34.00 4.41 bcde 4.40 abc 4.07 abcdeW14 2008 B 84 NA 45.00 4.42 bcde 4.47 abc 3.89 abcdeW15 2009 I 89 S 24.99 4.45 bcde 5.00 a 4.39 abcdeW16 2011 E 82 NA 10.00 4.47 bcde 4.51 abc 5.11 abcdW17 c 2009 F 95 G 19.99 4.47 bcde 4.88 a 5.70 a

4.96 abcdW18 2007 G 98 DG 70.00 4.54 abcde 4.39 abc 3.68 cdeW19 2010 F 87 B 22.00 4.56 abcde 4.91 a 5.07 abcdW20 2010 B 94 G 19.99 4.64 abcde 4.17 bc 4.93 abcdW21 2007 H 83 NA 29.00 4.66 abcde 4.46 abc 5.68 abW22 2010 F 83 NA 13.00 4.71 abcde 4.62 abc 4.50 abcdeW23 2010 E 89 S 14.00 4.72 abcde 4.33 abc 4.43 abcdeW24 2009 A 88 S 28.00 4.76 abcde 4.72 ab 4.96 abcdW25 2008 D 82 NA 32.00 4.78 abcde 4.60 abc 4.00 abcdeW26 2009 C 83 NA 59.00 4.83 abcde 4.07 bc 3.39 deW27 2001 H 92 S 45.00 4.85 abcd 4.01 c 3.04 eMin 2001 82 9.99 0.74 d 0.71 d 1.83 d

Max 2011 98 70.00Median 2009 89 26.95

a A North Coast includes everything except Napa and Sonoma; B Sonoma County, C Napa County, D Greater Bay Area, E North Central Coast, F South CentralCoast, G South Coast, H Sierra Foothills, I Lodi/Woodbridge Grape Commission.

b DG Double Gold; G Gold; S Silver; B Bronze; NA No Award.c

Three wines were presented twice to the experts.d Honestly signicant difference (HSD) according to Tukey.

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age 37 17 yrs (mean s.d.)). Panelists were recruited via emailfrom the UC Davis afliates, including students, staff, and retirees,and gave oral consent to participate in the study. Panelists receivedsnacks after each session and a gift card at the end of the study as atoken of appreciation.

Six one-hour training sessions over a period of two weeks wereheld where panelists were exposed to subsets of the 27 wines to

create, rene and gain consensus on the aroma, taste and mouth-feel attributes which described the perceived differences amongthe wines ( Table 2 ). Each wine was seen blind at least once duringthe training. Training of the DA panelists was evaluated by blindrecognition exercises of the reference standards at the end of thetraining, and all panelists successfully succeeded in this beforewine evaluation took place. After training was completed, all 27wines were evaluated in triplicate in individual sensory booths un-der red light and positive air pressure. 25 mL of wine was served inblack standard wine tasting glasses, labeled with a random three-digit code, and panelists were instructed to expectorate the sam-ple, and rinse with deionized water (Arrowhead, Nestle, Stamford,CT) in between samples. Panelists rated each attribute on a com-puter screen using an anchored, unstructured line scale providedby FIZZ (version 2.47B, Biosystmes, Couternon, France). A Wil-liam-Latin Square block design was used to control for carry-over

effects, with 67 wines per block and a total of 12 blocks, evaluatedover a period of 4 weeks.

During the rst training, panelists were screened for color vi-sion deciencies (redgreen color blindness) using pseudo-iso-chromatic plates (American Optical Corporation, Ontario, Canada)and were considered to have normal color vision if they correctlyidentied six out of 7 plates. During the remaining training ses-

sions they were encouraged to note color differences. Only a fewwines differed in color while most of the wines presented duringthe training sessions were very similar, so the panel decided tocomplete a free sorting task for color in triplicate. During the last3 evaluation sessions panelists were asked to sort 30 samples(27 wines with three blind duplicates) according to color into asmany groups as they wished, but at least two and a maximum of 29 groups. Two individual tasting booths with dened illuminationconditions were set up with 30 clear standard wine tasting glasses,labeled with random three-digit codes, containing 25 mL of wine,and covered with a transparent plastic lid, as well as an evaluationsheet. The evaluation table had an off-white background color andwas illuminated by two vertically mounted halogen lamps, 1.4 mdistant from the table surface and 30 cm apart from each other.The halogen lamps were used at maximum luminous intensity(1580 cd, MR16 Superline Reekto, Ushiro America, Inc., Cypress,

Table 2

Reference standards for the sensory attributes used in the DA panel. All attributes were anchored with the words low and high at the end of the unstructured line scale.Franzia Vintners Select Cabernet Sauvignon (Ripon, CA) was used as base wine.

Aroma standardsOverall aroma

intensityVerbal description: the intensity of the wine smell

Earthy Tbsp. potting soil (Black Gold, Bellevue, WA) + 1 g Orchid bark (Black Gold) + fresh champignon mushroom + 5 drops waterFresh veggie 1 cut green bean + 2 frozen green pepper strips (C+W Birds Eye, Peoria, IL) + 1 fresh Broccoli rosette in 25 mL base wineFresh green herbal 0.05 g dried Dill (The Spice Hunter, San Luis Obispo, CA) + 0.1g dried herb mix (Davis Co-Op, Davis, CA) in 30 mL base wine

grassy 4 Fresh grass clippingsminty 2 Crushed fresh mint leaves + 0.5 mL Eucalyptus solution (3 drops eucalyptus essential oil in 100 mL water)

Canned veggie 2 mL canned asparagus brine (Green Giant, Minneapolis, MN) + 2 mL canned green bean brine (Green Giant) + 1 mL canned sweet corn brine (BestYet, Keene, NH) in 15 mL base wine

Floral 0.05 g dried lavender (Davis Co-Op) + 0.2 g dried red rose buds (Davis Co-Op) + 2 mL violet solution (2 drops violet essential oil in 100 mL water) in10 mL base wine

Dried fruit dried fruit 1 Cut dried g (SunMaid, Stockton, CA) + 10 raisins (SunMaid) + 1 cut dried apricot (SunMaid) in 15 mL base wineoxidized 5 mL Marsala Superiore riserva 10anni DOC (Marco de Bartoli) in 10 mL base wine

Soysauce 12 mL soy sauce (Hisakawa, Golding Farms Foods, Winston-Salem, NC) in 15 mL base wineYeasty 0.1 g SuperFood Yeast Nutrient (Gusmer Enterprises, Fresno, CA) in 15 mL base wineSweet aroma honey/

caramel0.25 mL vanilla extract (Kirkland, Costco) + 1 Tbsp. Mrs. Richardsons Butterscotch caramel (Frankfort, IL) + 1 Tbsp. honey (LienertsMountain wildower honey, Sacramento, CA)

chocolate 0.5 g grated 70% chocolate (BRIX, Rutherford, CA) + 0.5 g 100% chocolate (Bakers, Krafts Food, Northeld, IL) in 15 mL base wineSpices 0.05 g ground cloves (McCormick, Hunt Valley, MD) + 0.06 g ground nutmeg (McCormick) + 0.07 g ground ginger (McCormick) + 0.2 g ground

cinnamon (McCormick) in 15 mL base wineRed fruit 1 frozen strawberry (Dole, West Village, CA) + 5 frozen raspberries (Dole) in 15 mL base wineDark fruit 1 Tbsp. blueberry spread (Cascadian Farms, Rockport, WA) + 5 mL black cherry juice concentrate (RW Knudsen, Chico, CA) + 1 Tbsp. blackberry jam

(Mary Ellen, Orrville, OH) + 0.5 mL black currant avoring (IFF, New York, NY) + 3 mL waterChemical Verbal description: The smell of ammonia and chlorinated swimming poolAlcoholic 1 mL 95% ethanol (Goldshield, Hayward, CA)Brett a 20 mL Cabernet Sauvignon (Walton 2006, Napa Valley, CA) + 0.05 g white pepper (McCormick)

Smoky 1 drop guaiacol (Acros Chemicals, Pittsburgh, PA, 99 + %) in 25 mL base wineBlack pepper 0.1 g ground black pepper (McCormick) in 15 mL base wineMusty/dusty musty 2 mL CE organic acid solution (Agilent, Santa Clara, CA) + 1 mL red wine vinegar (Star, Fresno, CA) in 15 mL base wine

dusty 1/8 tsp. 100% Montmorillonite European clay (Now Personal Care, Bloomingdale, IL) + 2 mL waterOak 0.15 g Evoak Premium dark roasted chips (Oak Solutions, Napa, CA) in 15 mL base wineSulfur burnt

rubber 1 mL 95% ethanol (Goldshield, Hayward, CA) in 15 mL wine

rotten egg hard boiled egg (boiled for 30 min) + 0.5 mL SO 2 solution

Taste and mouthfeel b

Sweet 10 g/L Sucrose (C+H, Crockett , CA)Sour 1 g/L L+ tartaric acid (Fisher Scientic, Pittsburgh, PA)Bitter 0.8 g/L Caffeine (SigmaAldrich, St. Louis, MO)Astringent 0.8 g/L Aluminum sulfate (McCormick)Hot 250 mL/L 40% VodkaViscous 1.5 g/L carboxymethyl cellulose (SigmaAldrich)

a Bett refers to the smell associated with a wine spoilage yeast, Brettanomyces bruxellensis. It is reminiscent of leather, sweaty horse, and barnyard.b

All standards were prepared in deionized water (Arrowhead, Nestle, Stamford, CT).

H. Hopfer, H. Heymann / Food Quality and Preference 32 (2014) 221233 223
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CA, USA) and had a color temperature of 3000 K, resembling thespectral distribution of a CIE standard illuminant A, but with moreyellow and red wavelengths. No specic evaluation procedure wasused, but panelists were told to be consistent in the way they eval-uate the color of the wines over the three days.

2.3. Consumer panelOne hundred and seventy-four consumers who reported that

they consumed wine regularly were recruited by email from thegreater Davis area, and came to the sensory laboratory at UC Davisfor one session to participate in the study. During a short introduc-tion about the tasks of the study the consumers gave oral consentbefore they proceeded into the sensory booths for the tasting. Allconsumers evaluated the wines the same day. Each consumertasted 6 wines and rated the overall liking and the overall qualityof each wine on a computer screen on an anchored, unstructuredline scale with the anchors Dislike extremely for the liking andVery low quality for the quality rating at the left end and Likeextremely and Very high quality at the right end of the scaleas provided by FIZZ, which also collected the data and convertedthem into two-digit values between 0 and 9. Consumers wereencouraged to expectorate the sample. The presentation orderwas a balanced incomplete block design with carry-over control,using the algorithm of Wakeling and MacFie (1995) . After eachwine was scored, consumers answered some demographic ques-tions regarding sex, age, income, and wine consumption. They alsoanswered 15 questions to determine their wine expertise. Consum-ers were assigned wine expertise status depending on the numberof correctly answered questions, low (6 or less correct answersout of 15), medium (711 correct answers) and high (12 ormore correct answers).

2.4. Expert panel

Twenty-eight wine professionals, the experts, satisfying thecriteria of ( Parr, White, & Heatherbell, 2004 ), were recruited to par-ticipate in a blind tasting of the wines. Experts included winemak-ers, enologists, cellar workers, wine consultants, enology teachersand other wine professionals working in the wine supportingindustry (e.g. cork supply, cooperage, external wine laboratories,etc.). One wine expert stated in the questionnaire to have been awine judge in the wine judgment, where he also tasted CabernetSauvignon wines. However, due to the great number of CabernetSauvignon wines entered in the competition (333) compared tothe subset used in this study (27) the chances are very small thatthis wine expert would have tasted the same sample set duringthe wine judgment.

The evaluation consisted of two separate sets of 30 wines(27 samples and three blind duplicates). With the rst set theexperts were asked for their overall liking of each wine, using a9-point category scale, labeled at the left end with Dislike extre-mely, in the middle with Neither like nor dislike and at the rightend of the scale with Like extremely. With the second set the ex-perts were asked to sort the wines into ve quality categories,ranging from lowest to highest quality. Each glass was coded witha random three-digit code, and each panelist was seated at a sep-arate table, equipped with the two sets of wines with differentlycoded glasses, evaluation sheets, water and a spit bucket. Finally,they were asked some questions with regards to demographics(age, sex) as well as wine industry related ones, such as job title,wine tasting frequency, wine industry experience and wine judg-

ment experience. We also asked them two open-ended questionsabout attributes they associate with a high and a low quality wine.

2.5. Data analysis

All data analyses were done in RStudio (version 0.97.551,RStudio, 2012 ), with the additional packages candisc ( Friendly &Fox, 2010 ), SensoMineR ( L & Husson, 2008 ), FactoMineR ( L, Josse,& Husson, 2008 ), missMDA ( Josse & Husson, 2012 ), pls ( Mevik &Wehrens, 2007 ), cluster ( Maechler, Rousseeuw, Struyf, Hubert, &

Hornik, 2013 ) and DistatisR ( Beaton, Fatt, & Abdi, 2013 ).Signicance testing using an alpha level of 5% was done on theDA data by multivariate analysis of variance (MANOVA) for thewine effect, followed by univariate analysis of variance (ANOVA)for each attribute using a three-way xed effect model with alltwo-way interactions ( wine W , panelist P , replicate R , WxP , WxR,PxR). For attributes with a signicant panelist effect and signicantpanelist interactions ( WxP ) a pseudo mixed model with the inter-action as the error term was used ( Gay, 1998 ).

Missing liking and quality score values in the consumer datadue to the incomplete block design of the study were imputedusing a regularized iterative PCA algorithm as proposed by ( Josse& Husson, 2012 ). Multiple imputation with principal componentanalysis (MI-PCA) was used to obtain a measure for the uncertaintyfor the imputation of the missing values as described in ( Josse,Pags, & Husson, 2011 ). MI-PCA estimates both a missing valueas well as a variability for the imputation. MI methods can be usedfor values missing at random, such as the missing consumer valuesin this study.

Once a complete data set was obtained all following data anal-yses used the imputed data set. Consumer segmentation based onthe liking and quality scores were obtained by hierarchical cluster-ing of the imputed data set, using Euclidean distances and Wardslinkage. The resulting clusters were chosen visually where a largedrop in the hierarchical tree height was observed. Hedonic liking(HL) and quality scores (Q) from the consumer clusters and aver-aged over all consumers, and the HL data from the expert panelwere analyzed by ANOVA for the wine and panelist effects, followedby post hoc analysis according to Tukey (Honestly signicant dif-ference HSD). Cluster segmentation was tested for equal propor-tions using a Chi-Square test for all demographical questions.

Various product space representations were obtained usingprincipal component analysis (PCA) for the DA data and internalpreference maps (IPM) with the HL and Q data from both the con-sumer and expert panels. The experts quality sorting data wasanalyzed with DISTATIS ( Abdi, Valentin, Chollet, & Chrea, 2007 ).

Correlation of the various data sets, foremost, correlating the HL and Q scores from both consumer clusters and experts to the DAdata, was done using Partial Least Squares Regression type 2(PLS2), with the DA attributes as the predicting and HL and Q scores as the predicted variables. The obtained model was cross-validated by a leave-one-out procedure.

3. Results and discussion

3.1. Obtaining product characteristics with Descriptive Analysis (DA)

After the MANOVA revealed signicant differences among thewines, the ANOVAs showed signicant differences in 17 aroma,two taste and two mouthfeel attributes among the 27 wines(P 6 0.05) ( Suppl. Table 1 ).

All signicant attributes were used to create the graphical prod-uct presentation with PCA of the covariance matrix, shown inFig. 1a and b: Along the rst principal component PC 1, explaining34.3% of the total variance, samples were to some extent separatedby their performance in the wine competition, reected in the

opposite direction of vegetal-green and chemical-earthy aromas onthe left hand side of the variables plot and fruity , oak and sweet aro-

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mas on the right hand side. Along PC 2, explaining additional 19.3%of the total variance, wines were separated due to taste andmouthfeel attributes, with astringency , bitterness and hot mouthfeelexplaining the positive PC 2 axis, opposite to sweetness on the neg-ative second principal component.

Especially wines W19and W16and, to a lesser extent,winesW22and W24 were driven by their canned and fresh vegetal and green

notes, while wine samples W5, W6, W11, W14, W18, W26 andW27 were rated high in the attributes Brett , rotten egg /sulfur , chem-ical and earthy , all attributes that point towards wine spoilage.

On the right hand side of the PCA product plot the DA panelrated wines W1W3, W7, W10, W12, W17, W23 and W25 highin various fruit (i.e. dried fruit, dark fruit and red fruit) and sweet (i.e. honey, caramel, vanilla) aromas as well as sweet taste. Lastly,wines W8, W15, W21 and W24 were mostly driven by an astringent and hot mouthfeel, bitter taste, alcoholic , smoky , spicy andoak aromas. Wine 19 was positively correlated to soysauce , andtwo wines (W13 and W20) were located in the origin of the prod-uct plot together with overall aroma , indicating a rather balancedsensory prole, without any particular attributes driving the sen-sory characteristics of these wines.

The aroma attributes oak , sweet aroma , red fruit , dark fruit , dried fruit and spices are all signicantly positively loaded on PC 1, whilechemical , earthy , fresh veg , canned veg , sulfur and Brett are all signif-icantly negatively loaded on PC 1 ( P 6 0.05). Similarly, on PC 2, themouthfeel attributes astringent and hot as well as bitter taste andchemical aroma are signicantly loaded on the positive PC 2 axis.

Similar ndings were reported for AOC Bordeaux and BordeauxSuperieur wines, in many cases Cabernet Sauvignon blends(Szolnoki & Hoffmann, 2011 ). The authors performed multiplelinear regressions between quality scores and DA attributes, andfound signicantly positive correlations between the quality scoresand the terms body , coffee, oak , as well as rose , jam and strawberry ,while Brett , mushroom , lactic /butter and bacon as well as acidity andoxidation were negatively correlated to quality scores.

3.2. Can color be used to predict quality?

ThetrainedDA panel also evaluated color differencesin a sortingtask as described in Section 2. Panelists sorted 30 wines (27 wines

with 3 blind duplicates) on three consecutive days, and data fromeach day was analyzed with DISTATIS ( Fig. 2af). In the DISTATISprocedure the individuals sorting data are used, thus, an RV coef-cient map of individuals agreement was calculated, and the DA pa-nel showed a partial agreement in the sorting task, indicated by thelower explained variance of the rst dimension of around 30%(Fig. 2b, d and f). Compared to e.g. 60% explained variance in a beer

sorting task ( Abdi et al., 2007 ), the DA panel was in less agreementwhen sorting the wines by color. However, the beers used in thestudy were more different in terms of beer style, and assessorsare more likely to agree in their sorting tasks. Additionally, inDISTATIS, the assessors importance for the compromise productmap is weighted based on the RV compromise map, and therefore,assessors that show less agreement with the others contribute to asmaller extent to the compromise map. So called a weights variedbetween the three replicates and assessors between 0.04 (J3, J11and J11 in the three replicates) and 0.080.09 (J4, J5 and J5 in thethree replicates). In all three replicates (around 25% of the varianceexplained in therst two dimensions of thecompromise plot) threeproduct clusters were found due to similarly perceived colors.Wines W1, W4, W5, W13, W18, W25 and W27 were found to besimilar in color, and form a group in the bottom right quadrant of the product plot ( Fig. 2a, c and e). Those wines were from the oldestvintages in the set (20012008). A second group made up of winesW6W8, W14, W15, W20, W21, W23, W24 and W26 represents themiddle-aged wines in the set, mostly from the 2009 and 2010 vin-tages, exceptfor W8 from the 2008 andW21 from the2007 harvest.A last, less tight group consists of all wines harvested in 2009 or la-ter, thus, representing the youngest wines (W2, W3, W9, W10,W12, W16, W17, W19 and W22). Wine W11 is positioned in thecenter of the plots, indicating an averaged color, most likely dueto its medium age (wine vintage, 2007).

Based on these results, color is an indicator of age, rather thanquality, as the DA panel grouped wines from the same vintagestogether, independent of the assigned quality scores in the winecompetition. This is in good agreement with the ndings of Machado (2009) , who found no correlation between the qualityratings of red wines and their color. However, we do not know if judges in the wine competition were able to judge the color of the wines, so the possibility exists that color was incorporated into

PC 1, 34.3%

P C 2

, 1 9

. 3 %

-4 0 4

- 3

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3

W1W10

W11

W12

W13

W14

W15

W16

W17

W18

W19

W2

W20W21

W22

W23

W24

W25

W26 W27

W3W4

W5

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Fig. 1. (a) PCA product and (b) variables plot using the DA attributes that differed signicantly among the wines ( P 6 0.05). Wines are color-coded according to theirperformance in the wine competition (green No Award; blue Silver or Bronze medal; gold Gold or Double Gold medal). Aroma attributes are shown in bold, taste

attributes are italicized and mouthfeel attributes are underlined. (For interpretation of the references to color in this gure legend, the reader is referred to the web version of this article.)

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the quality assessment during the competition. To some extent col-or can be a quality predictor, as winemaking and storage condi-tions, such as oxygen amount, inuence the color of red wine(Caill et al., 2010; Wirth et al., 2010 ), however, in this set, the im-pact of vintage was driving the observed sensory differences in redwine color.

3.3. Measuring the overall liking and quality with consumers

A total of 174 consumers were recruited to taste subsets of thewines and rate the overall liking (HL) and overall perceived quality

(Q). Due to the use of a balanced incomplete block design, missingHL and Q values were imputed by a regularized iterative MI-PCA asindicated in the methods section. An ANOVA on overall liking andperceived quality values revealed signicant differences among thewines ( Table 3 , P 6 0.05). Hierarchical clustering on the imputeddata set, separately for the HL and the Q values, led to four con-sumer segments for both data sets (HL1HL4 and Q1Q4), and sig-nicant differences between the clusters were found for HL and Q scores. Chi-Square tests for equal proportions for all demographicalquestions were not signicant ( P > 0.05), indicating no segmenta-tion due to demographics but only due to different hedonic likingand quality perceptions ( Table 3 ).

A graphical representation of the different consumers and therelation between their HL and Q scores to the wines were studied

with an internal preference and internal quality map using multi-dimensional preference mapping ( Delgado & Guinard, 2012 ). The

resulting product and variable plots are shown in Fig. 3ad, andthese results will be combined with the demographical separationof the individual consumer segments shown in Table 3 .

Consumers were equally distributed among the wines, indicat-ing a broad range of liking and quality perception. Additionally,due to the imputation of the missing data, circles, representingthe uncertainty of the imputation, are large, and overlap for mostwines. However, if we averaged the raw data for each wine, asshown in Table 1 , most wines were not considered signicantlydifferent using Tukeys post hoc test, and this fact is also shownin the internal preference and quality maps ( Fig. 3), therefore,

we believe that the imputation of the missing values is a goodapproximation. However, only after the imputation, cluster analysisand demographic exploration of the consumer data was possible.

For the internal preference map ( Fig. 3a and b) explaining over80% of the total variance within the rst two dimensions clustersliking scores differed signicantly ( P 6 0.05) and separated thewines into the four quadrants of the product map, with 9 winesin the top left quadrant (W3, W5, W8, W9, W13, W14, W16,W23 and W27) being liked the most by cluster HL4, a consumersegment of 42 consumers. Consumer in this segment liked thewines overall the most (average of 6.24), and reported the lowestpercentage of less than $35,000 yearly income. Most people in thiscluster earn between $35,000 and $75,000 per year. Consumers inthis cluster reported to consumer wine 15 times per week, and

showed the highest percentage of medium wine expertise of allHL clusters. Among the wines preferred by HL 1 were 1 Double

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Fig. 2. (a) DISTATIS product plot and (b) RV consensus plot with each DA panelist for the rst sorting replicate, (c) DISTATIS product plot and (d) RV consensus plot with eachDA panelist for the second sorting replicate, (e) DISTATIS product plot and (f) RV consensus plot with each DA panelist for the third sorting replicate. Wines are color-codedaccording to their performance in the wine competition (green No Award; blue Silver or Bronze medal; gold Gold or Double Gold medal). (For interpretation of the

references to color in this gure legend, the reader is referred to the web version of this article.)



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Gold, 2 Gold, 3 Silver and 3 No Award medals (see Table 1 ). Fivewines (W1, W4, W7, W10 and W24), including 1 Gold, 3 Silverand 1 No Award, were the wines that consumers in HL cluster 2liked the most. HL2, a consumer segment of 42 people, had thehighest percentages of under 30 year olds, and thus, the highestpercentage of under $35,000 yearly income. Consumers in this seg-ment drink wine less frequently, and reported 14 drinking occa-sions per month. Most HL2 consumers had low or medium wineexpertise (81%). Wines positioned in the bottom right quadrant(W2, W6, W18, W19, W21, W26) were rated highest in liking byconsumers in the HL 3 segment ( n = 42). Wines in this quadrant in-clude 1 Double-Gold, 2 Silver, 1 Bronze and 2 Non-awards. HL3consumers were very similar to consumers in HL2, but had a lowerpercentage of under 30 year olds and less people in this segmentearned less than $35,000 per year. The majority in this cluster isunder 40 years old. HL3 consumers showed the highest percentageof more than 5 drinking occasions per week, and also include thesecond highest percentage of high expertise.

All remaining wines (3 Gold (W11, W17 and W20), 2 Silver(W12, W15), and 2 NA (W22, W25)) are located in the bottom leftquadrant of the internal preference map. Most consumers in theHL1 segment rated these wines highest, however, this consumer

segment ( n = 48) rated the wines overall signicantly lower thanthe other three segments ( Table 3 ). HL1 had the highest percentageof over 50 year olds, and also the highest percentage of over$75,000 yearly income. Nearly 50% of this segment drinks wineat least once a week or more, and 63% showed medium wineexpertise. Across all clusters, a similar split between females andmales of 4245% males was observed.

A different picture to the internal preference map is shown inthe internal quality map using the perceived quality ratings of the consumers ( Fig. 3c and d), despite explaining a similar percent-age of the total variance within the rst two dimensions (>80%):Again, four consumer segments (Q1Q4) were found with hierar-chical clustering, using Euclidean distances and Wards linkage(Table 3 ).

Signicant differences in the overall quality ratings of the wineswere found between the four segments, with Q1 liking the wines

signicantly lower than the other three segments(Q1 < Q2 < Q3 < Q4; Table 3 ).

Wines positioned in the top left quadrant of the internal qualitymap ( Fig. 3c) included 1 Double-Gold (W8), 2 Silver (W12, W23), 1Bronze (W14) and 2 NA (W1, W22) wines. Consumers of segmentQ3 ( n = 57) rated these wines highest in quality. This segment hadthe highest percentage of under 30 year olds, and the lowest per-centage of 3039 year olds of all consumer clusters. More than 3= 4in this cluster reported a yearly income of less than $50,000, anda similar percentage reported to drink wine between once a monthand up to ve times a week. More than half of these consumers(65%) were classied as medium experts in their wine knowledge.

Wines in the top right quadrant, including 3 Gold (W3, W10,W17), 3 Silver (W2, W6 and W15) and 2 Bronze (W7, W19) wines,were rated highest in quality by most consumers in segment Q1(n = 37) and a few consumers of segment Q3. In Q1, the highestpercentage of over 60 year olds (11%) of all clusters was found,and thus, the highest percentage of yearly incomes over $75,000was reported as well. However, nearly half (46%) in this segmentwere under 30 years old, and 62% in Q1 earn less than $35,000per year. Wine consumption is nearly equally distributed amongthe categories, but the highest percentage of drinking wine more

than 5 times per week of all clusters was found in this segment.Consumers were either low or high in wine expertise.

The bottom right quadrant was made up by 6 wines (1 Double-Gold, 1 Gold, 2 Silver and 2 NA), and particularly consumers in seg-ment Q2 rated these wines high in quality. Q2 consumers were thesecond largest segment ( n = 50), and interestingly, cluster closertogether than any of the other clusters, thus, show a more similarquality perception than other consumer segments. Forty percent of these consumers were between 30 and 50 years old, and more thanhalf reported a yearly income of less than $35,000, together with14 wine consumptions per month. This segment has the highestpercentage of medium wine expertise, and was interestingly theonly segment that was less equally divided between females andmales (38% males vs. more than 40% in the other clusters).

The last consumer segment, Q4, included 30 consumers, andhad the highest percentage of over 50 year olds, with over 50%

Table 3

Demographical distribution of the four consumer segments, separated for the HL and Q scores. Letters denote signicant differences in HL and Q between the consumer segmentsby post hoc analysis according to Tukey ( P 6 0.05).

Overall HL1 HL2 HL3 HL4 Overall Q1 Q2 Q3 Q4

n 174 48 42 42 42 174 37 50 57 30HL 3.06 c 4.12 b 5.53 a 6.24 a 4.58 2.88 d 3.77 c 5.25 b 6.73 a

Gender Male (%) 43 42 45 43 43 43 43 38 46 47Female (%) 57 58 55 57 57 57 57 62 54 53

Age60 years (%) 5 8 2 7 2 5 11 4 4 3

Income$75 k (%) 9 15 5 5 10 9 11 10 7 7

Consumption5 week (%) 15 19 7 21 12 15 22 14 11 17

ExpertiseLow (%) 23 25 33 21 12 23 32 18 23 20Med (%) 63 63 48 62 79 63 49 68 65 67High (%) 14 13 19 17 10 14 19 14 12 13

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consumer. However, we also found that consumer were not able tond the same quality pattern as the wine judges, as Gold medalwines and No Award wines were liked similarly and perceived sim-ilarly in quality as well.

3.4. Experts view on liking and quality

The 28 wine professionals, the experts, showed signicant dif-ferences in the liking of the wines ( P 6 0.05, Table 1 ). Two NoAwards and one Silver medal wine (W1, W5 and W27) were likedleast, while a Gold medal wine (W17) was liked the most. Weserved the experts 30 wines with 3 blind duplicated wines (W5,W12 and W17), and found that although the wine pairs did not re-ceive identical ratings the ratings were within the HSD range, thus,not signicantly different from each other ( P 6 0.05).

Similarly to the consumers, we created an internal preferencemap based on the HL scores and an internal quality map usingthe DISTATIS algorithm on the quality sorting data ( Fig. 4ad).Experts HL scores were in higher agreement with each other than

the consumer scores, shown in the variables plot of the internalpreference map (compare Fig. 3b vs. Fig. 4b) where expertsloadings group together along the positive rst dimension. Winesthat were liked most are located along the positive rst dimensionof the internal preference product map, which explained around35% of the total variance ( Fig. 4a). Wines liked most includedW6-W10, W16, W19, W20, W24, W29 and W30 as well as the

two blindly served pairs W12ab and W17ab. Wines W3, W15,W22 and W23 were rated medium in liking, and all other wines,located on the negative rst dimension, were liked least. All pairsof the three duplicated wines (W5, W12 and W17) were close to-gether, indicating that experts perceived them similarly.

From the sorting task where experts were asked to sort thewines into 5 quality categories, ranging from low to high, a similarproduct map was found ( Fig. 4c and d). In the DISTATIS procedurethe individuals sorting data are used, thus, an RV coefcient mapof individuals agreement can be calculated. The DISTATIS productmap, a kind of internal quality map, is shown in Fig. 4c. Generally,wines that experts liked similarly were also grouped together in

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Fig. 4. (a) Internal preference map and (b) loadings plot using the HL scores of the experts (each line represents one expert). (c) Internal quality map using the experts qualitysorting data in combination with the DISTATIS algorithm. (d) Experts RV consensus plot (each dot represents one expert). Wines are color-coded according to their

performance in the wine competition (green No Award; blue Silver or Bronze medal; gold Gold or Double Gold medal). (For interpretation of the references to color inthis gure legend, the reader is referred to the web version of this article.)

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terms of quality, e.g. wines W6W10, W12ab, W16, W17ab, W19W21 and W24 grouped together in both product maps, while theless liked wines W1W5, W11, W13W15, W18, W25 and W26were similarly rated in quality. The experts showed low agreementamong each other, shown by the RV map ( Fig. 4d), with an ex-plained variance of 15% in the rst dimension. However, all of themcluster together in a tight group similar to the DA panel in the color

sorting task, and the a

weights ranged between 0.024 and 0.043.In a last step all experts were asked to answer some questionswith regards to demographics and their wine tasting experience(Table 4 ). They were also asked to describe what makes a high ora low quality wine, using two open questions Which attributesdo you associate with a high quality wine? and Which attributesdo you associate with a low quality wine? . The majority of the ex-perts were male and over 30 years old, and had at least 5 yearsof professional experience in the wine industry, over 70% reported10 and more years of experience. Winemakers and assistant wine-makers made up over half of the participating experts, and nearly90% of the experts reported to taste at least once or more per week,mainly due to the fact that 86% report that wine tasting is part of their job description. Several wine tastings per week have beenpart of 50% of the experts daily life for the last 10 years or more.However, only a third of the experts use their tasting experienceoutside their job, and participate for example in wine shows orwine competitions. One expert reported to have participated as a judge in the 2012 CA State Fair Commercial Wine Competition.

In the open questions about the attributes of high and low qual-ity wines, most experts used general terms such as balance (14 outof 25, 3 experts did not answer these questions), lack of defects (9)and complexity (3) to describe a high quality wine. One expert ex-plained balance = visually nice in combination with intense aromaand lasting nish . Another winemaker described complexity aslayers of interesting qualities, concentrated avors, appropriatemouthfeel for the wine style.

Additionally, more tangible terms were given, including true/correct varietal character (5), soft/smooth/round tannins (6), cor-rect mouthfeel (2), big/voluminous aroma (2), structure/texture(3), length/nish (4) and volume/weight (3). Four experts de-scribed the importance of the balance between fruit and oak, whileadditional four experts considered the presence of fruit essentialfor wine quality. Also, wine professionals use hedonic terms forwine quality, such as pleasure (2), yumminess and drinkability,and one expert described a high quality wine as pleasure that out-weighs aws. One expert each thinks integration, elegance,uniqueness, sense of place and typicity are attributes of a highquality wine, while another expert summarized it as quality thatis appropriate for the price.

When describing a low quality wine, most experts (18 out of 25)noted defects , faults and aws , and some gave examples, such as

aromas associated with Brettanomyces spoilage, volatile acidity,oxidation, reduction or other microbial spoilages. Coarse or harshtannins were mentioned by 10 experts, followed by lack of balance(9), and in some cases, more specically, lacking balance betweenfruit and oak characters or between sweetness and acidity. Otherdescriptions related to winemaking were overly or unbalancedsweetness (7), too much acid (7), atypical and/or lacking aromas

and avors (9), too much and/or unintegrated oak (8) , lack of body/ thin/short/at (7), and bitterness (3). Two experts noted the lack of pleasure , and one expert associated simplicity with a low qualitywine.

Overall, the experts provided insight into their quality assess-ment of red wines, and seem to combine a rather objective frame-work with descriptive terms with personal preference whenevaluating wine quality. It could be that due to their training wineexperts are more able to describe what they like, and which attri-butes they associate with high quality red wine.

3.5. Predicting hedonic liking and quality ratings by Descriptive Analysis

Correlating various data set to each other to understand whichvariables are driving liking and which are not correlated was thelast step in our study. Due to the nature of sensory data, i.e. mul-ti-collinearity of DA attributes is typically the case not the excep-tion, partial least squares (PLS) regression is an ideal method asin PLS the covariance of both the predicting and the predicted vari-ables is modeled. In our case we used all signicant DA attributesto predict the HL and Q ratings from the averaged consumers, theconsumer segments and the experts.

The reasoning behind that approach is several-fold: First, wewanted to understand if DA proles could predict liking and per-ceived quality for both untrained and experienced wine tasters.Secondly, the question was whether hedonic liking would correlateto perceived quality or whether these two concepts would be inde-pendent from each other, and lastly, if different populations dif-fered in their liking and quality scores, and, if these differencescould be correlated to particular sensory attributes. Since we usedthe combination of all liking and quality parameters at once, a PLS2algorithm was used.

Table 5 shows the summary of the PLS model. For an explainedvariance of the predicting matrix ( X ) of over 75%, the rst 8 dimen-sions of the model are needed, which predict over 50% of the pre-dicted variables ( Y ). However, within each of the predictedvariables, large differences in the prediction quality were found.For examples, while the hedonic liking and the quality ratings of the experts (HLexp, Qexp) as well as the averaged consumer liking(HLcons) could be predicted to around 50% with the rst two latent

Table 5

PLS regression model summary, showing the quality of the model for the rst 10 model components (LV), i.e. percentages of the total explained variance for the sum of thepredicting variables ( X ), the average over all predicted variables ( Y ), and each of the predicted variables (HL14, HLcons, HLexp, Q14, Qcons, Qexp).

(%) LV 1 LV 2 LV 3 LV 4 LV 5 LV 6 LV 7 LV 8 LV 9 LV 10

X 25.5 40.0 52.4 58.7 65.7 72.1 74.4 79.1 84.1 86.9Y 13.2 22.2 27.0 35.5 42.9 47.4 53.8 57.4 59.5 63.1HLcons 22.6 25.2 29.7 38.0 39.0 39.2 56.0 62.6 62.6 63.0HL1 1.9 3.9 11.9 16.8 21.5 33.8 42.8 45.7 53.5 59.2HL2 0.1 0.6 5.2 18.5 19.8 28.0 45.6 50.2 58.4 61.1HL3 1.4 3.7 21.7 39.6 45.3 53.1 57.1 57.8 58.4 64.5HL4 0.5 2.2 22.7 38.6 45.4 54.4 57.2 58.5 59.1 67.4HLexp 39.2 55.8 55.9 59.5 64.6 67.8 68.2 68.6 70.1 72.6Qcons 38.6 46.7 46.7 53.7 53.7 56.1 60.7 73.0 73.0 73.2Q1 3.4 14.7 14.9 27.7 32.3 36.8 39.0 45.0 45.8 50.4Q2 17.4 32.9 33.2 33.3 50.3 50.5 60.4 60.4 61.9 65.0Q3 0.6 16.0 16.3 20.2 34.4 35.9 41.9 44.5 44.5 50.6Q4 1.0 14.2 14.4 24.1 32.7 37.5 39.7 44.8 45.1 49.0Qexp 31.4 50.3 51.1 56.6 75.7 75.8 77.0 77.7 81.1 81.4



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variables (LV) of the model, the consumer liking was very poorlymodeled, with only 25% or way less of the variance explained.

This fact is also visually apparent in Fig. 5b, where the expertsand the quality scores of the consumers are positioned on the out-side of the correlation plot. All consumer segment variables (HL14and Q14) were only marginally explained by the PLS model, withslightly better prediction of the quality scores than the HL scores.This is also reected in the validation plot ( Suppl. Fig. 1 ) whereno minimum was found within the rst 20 model components inthe root mean squared error of prediction (RMSEP) for the con-sumer variables, while both expert variables as well as the qualityratings of the averaged consumers show a minimum after 24components.

Plotting the product scores and variable correlations next toeach other ( Fig. 5a and b) one can see that most gold and doublegold wines (except W18) are located on the left hand side or closeto the center of the score plot, showing a high positive correlationto the HL and Q scores of the experts and the averaged consumers.It seems that the liking and the quality ratings of the experts aredriven by the presence of various fruit attributes ( red fruit , darkfruit) as well as oak and sweetAroma and absent or only marginallydetectable vegetal-green , chemical , earthy , sulfur and Brett aromas.This is also supported by the open-ended questions about highand low quality wine descriptors, where most experts associatedlow quality with the presence of Brett, chemical, and reductionaromas, and in contrast, described the presence of fruit and oakas a characters in a high quality wine. It seems that experts are ableto correlate perceived quality and liking to dened sensory attri-butes, thus, making the PLS regression model a more accurate

one when experts are included, rather than using untrained con-sumers only. However, for the averaged consumers as well as theexperts, liking was highly correlated to perceived quality, indicatedby the close proximity of the two variables in the correlation plot.This nding is in good agreement with ( Lawless et al., 1997 ) whofound similarly that wine consumers hedonic scores were posi-tively correlated to hedonic scores of wine experts, and that wineexperts showed a high correlation between hedonic liking scoresand a 20-point quality scores.

4. Conclusions

Based on the DA results we can assume that the judges at the

wine competition awarded Gold and Double Gold medals to winesthat showed a balanced avor prole with detectable aromas of

fruit and oak and absent or only marginal present notes of vege-tal-green, chemical, earthy or sulfur characters. Similarly, mostGold and Double Gold wines showed lower scores in astringency,hot mouthfeel and bitterness, and higher sweetness. However,wines that were characterized similarly in the DA did not performequally well in the Wine Judgment, indicating a high inconsistencyamong the wine judges compared to the trained panel. This is inaccordance with the observations of Hodgson (2008, 2009) who re-ported a lack of concordance among different wine competitions.This fact is not too surprising, as a trained sensory panel spendsseveral training sessions on both the sample set as well as aroma,taste and mouthfeel references, in addition to the replicate blindevaluation of each sample. However, a similar improvement inconsistency due to some form of training could be expected withwine experts too, based on the results of Tempere et al. (2011),Tempere, Cuzange, Bougeant, Revel, and Sicard (2012) . Theyshowed that wine experts could improve their olfactory sensitivi-ties when repeatedly exposed to (or trained with) specic odor-ants. Using short-term, repeated exposure to the odorant allexperts showed signicantly improved detection of the odorant.The authors concluded that screening, training and monitoring of wine experts are important factors for improved quality controlof wine. Another approach to improve consistency among wine judges was proposed by Gawel and Godden (2008) who recom-mended replicate assessments of the wines in competitions.

For the 174 consumers that rated the hedonic liking and per-ceived quality of the studied wines, signicant differences werefound in liking and perceived quality ratings, and consumers couldbe segmented into four clusters due to similar liking and quality

ratings, after missing values were imputed with MI-PCA. Clustersdiffered only signicantly due to the liking and quality ratings,not due to any of the collected demographical information, suchas age, gender, income, wine consumption, or wine expertise. Con-sumer segments were separated in the internal preference map,indicating that each segment preferred different wines. This isnot too surprising as all wines in the study are commercial prod-ucts, thus, reecting the broad range of consumer preferences.However, these results also indicate that some consumers likingpatterns are opposite to experts quality perceptions. A similarobservation was made by Machado (2009) who found consumersegments that did not like highly rated dry red wines.

Twenty-eight wine experts (wine professionals working inNorth California), different from the wine experts that evaluated

the wines in the wine competition, tasted all 27 wines and ratedtheir liking and perceived quality in a similar way to the consumer

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Fig. 5. (a) PLS score plot showing the wines color-coded according to their performance in the wine competition (green ... No Award; blue Silver or Bronze medal; gold Gold or Double Gold medal). (b) PLS correlation plot showing both the predicting variables (DA attributes, taste attributes are italicized, mouthfeel attributes are underlined)and the predicted variables (hedonic liking (HL) and quality ratings (Q) for the four consumer clusters (14), averaged over the consumers (cons) and the experts (exp)). (Forinterpretation of the references to color in this gure legend, the reader is referred to the web version of this article.)

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group. The experts found signicant differences among the winesin liking, and conrmed the wine judgment ratings only to somedegree, with liking a Gold medal wine the most, and two No Awardwines and one Silver medal liked the least. Wine experts weremore in agreement about the wines than consumers, as the inter-nal preference map and the internal quality map using the expertsevaluations showed a similar picture, with wines that were per-

ceived as high quality wines being also the most liked ones by allwine experts. However, most Gold and Silver wines were not sig-nicantly different from each other, raising the question if the as-signed differences in the medals really reect perceivable sensorydifferences rather than just differences due to serving order and/orrandom effects. Based on our results, wine experts were not able torepeatedly nd differences among the wines in a similar way to thewine judgment.

Comparing all three populations (trained panel, consumers andwine experts), it is interesting to note that only the experts likingand quality ratings could be sufciently modeled by the DAdescriptors, indicating that the experts based their evaluations onobjective, descriptive attributes, while the consumers were lessable to do so. The average over all consumers showed a similar pat-tern to the experts, with very similar liking and quality ratings,while none of the consumer segments was positioned close tothe experts in the PLS correlation plot. Additionally, none of thesecluster were well described by the PLS model.

Overall, in contrast to other food products, such as peaches andnectarines ( Delgado, Crisosto, Heymann, & Crisosto, 2013 ), con-sumer liking and perceived quality ratings could not be modeledwell by DA, while wine experts seemed to use a similar constructto evaluate liking and quality of wines. This could be the resultof their constant exposure to the product, together with the neces-sity to describe their perception in a more objective way whencommunicating with others. This result is also in agreement withthe nding of a more analytical assessment of wine quality by wineexperts compared to novices ( DAlessandro and Pecotich, 2013 ).

In conclusion, wine quality does not only have several dimen-sions, including extrinsic and intrinsic factors, it seems that winequality is also a highly variable subject. Although we could identifysome sensory attributes that wine experts are looking for in highquality red wines, it seems that wine judges were not able to con-sistently apply these standards when judging wines. This can mostlikely be attributed to the large number of wines entered and eval-uated in the wine competition, and some kind of quality control inwine competitions is highly recommended. However, we alsofound that consumers span a broad range of liking, and even alow quality wine, from an expert and judge perspective, can beappreciated by some consumers. However, the general publicmight not be able to communicate their preferences as well aswine experts, but they are clearly capable of tasting and makinga choice, so it is in the wine industrys best interest to come up

with constant wine quality.

Acknowledgements

We would like to acknowledge the nancial support from theAmerican Vineyard Foundation (grant # 523 (2012) Judging WineQuality) and Jerry Lohr for making this work possible. A big ThankYou to all panelists and experts for their time and efforts, as well asChristine Wilson, Meredith Bell and Anna Hjelmeland for theirhelp. We acknowledge the wineries who donated or discountedthe wines used in this study, including Alexander Valley Vineyards,Baily Vineyard & Winery, Biltmore Estate Wine Company, CameronHughes, Cecchetti Wine Company, Inc., Convergence Vineyards,

Dogwood Cellars, Fields Family Wines, Le Vigne Winery, MettlerFamily Vineyards, Monte de Oro Winery, Muscardini Cellars,

Perrucci Family Vineyards, Sean Minor Wines, The Wine Group,Tricycle Wine Co., V. Sattui Winery and numerous others.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.foodqual.2013.10.004 .

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