creacion de un espacio con el metodo de ultra flash

8/18/2019 creacion de un espacio con el metodo de ultra flash

1/24

jss_216 372..395

CONSTRUCTION OF A PRODUCT SPACE FROM THEULTRA-FLASH PROFILING METHOD: APPLICATION TO 10 RED

WINES FROM THE LOIRE VALLEY

LUCIE PERRIN1,2,3,4

and JÉRÔME PAGÈS3

1UMT Vinitera Laboratoire GRAPPE

Groupe ESA55 rue Rabelais, BP 30748, 49 007 Angers, France

2 Interloire73 rue Plantagenêt, 49 100 Angers, France

3 Laboratoire de Mathématiques Appliquées Agrocampus Rennes/IRMAR

65 rue de Saint Brieuc CS 84215, 35 042 Rennes Cedex, France

Accepted for Publication August 19, 2008

ABSTRACT

This study proposes a methodology for the collection and the analysis of

free-text descriptions. Our hypotheses were that the high number of wordsusually required in textual data analysis could be compensated by (1) theexpertise of the judges; and (2) by collecting free descriptions in the context of a Napping® evaluation (Ultra-ash proling [UFP]). This method wasapplied using 14 wine experts, who evaluated 10 red wines twice. The independent analysis of the words thus obtained is interpretable and rich of information. The comparison with the characterization obtained from an independent classical proling both supports the results of the UFP and shows thespecic contribution of this method. Moreover, the minimal quotation fre-

quency from which the items were introduced in the analysis is discussed. Inthe present example, the robustness observed whatever the threshold selected is an argument in favor of the stability of this method.

PRACTICAL APPLICATIONS

The example studied shows that it is possible to get an interpretableproduct space from free-text comments provided by a limited number of

4 Corresponding author. TEL: +33 2 23 48 58 85; FAX: +33 2 23 48 58 71; EMAIL: [email protected]

DOI: 10.1111/j.1745-459X.2009.00216.x

Journal of Sensory Studies 24 (2009) 372–395.© 2009, The Author(s) Journal compilation © 2009, Wiley Periodicals, Inc.

372


2/24

judges, when judges are experts and when descriptions are collectedin the context of a Napping® evaluation. Beyond the example, this studyproposes a methodology for the collection and the analysis of free-textcomments.

INTRODUCTION

The most common tool used to evaluate the sensory properties of foodand beverage products consists of establishing conventional sensory proles.Quantitative Descriptive Analysis (QDA®) (Stone et al. 1974), or its adapta-tions, provides a complete description of the sensory sensations. It is alsoknown to be accurate, particularly when the training time is increased (Woltersand Allchurch 1994; Chambers et al. 2004; Labbe et al. 2004), as well as easyto interpret (Lawless and Heymann 1998). Yet, numerous authors underlinedsome limits of the method and developed various alternatives. For example,in order to avoid the panel training, Williams and Langron (1984) developedFree Choice Proling and Dairou and Sieffermann (2002) and Delarue andSieffermann (2004) developed Flash Prole.

In the wine industry, product characterization is usually performed bywine professionals and their judgments are taken as references. Wine pro-fessionals are not formally trained, as usually done in QDA, but thanks totheir high level of expertise, consistent wine characterizations can beobtained by scoring a free list of attributes (Perrin et al. 2007) or a pre-established list.

However, in the wine industry, the experts are traditionally asked todescribe the wines with free-text comments, without scoring. In addition, theanalytical sensory tools are sometimes questioned, since the panelists areforced to dissect their perception (Saint-Eve et al. 2004; Cartier et al. 2006),hence the interest of holistic methods, such as the free sorting task (Heymann1994; Lawless et al. 1995; Tang and Heymann 2002; Saint-Eve et al. 2004) orthe Napping® method (Pagès 2005). Completed by words (the free descriptionrealized in a Napping® positioning context is called Ultra-ash proling[UFP]), these methods could be viewed as interesting alternatives to prolingand the free descriptions as more obvious.

Several statistical tools, such as Correspondence Analysis (CA) (Greena-cre 1984; Escoer 2003), and software such as the Alceste program (Reinert1986), are specially used for the textual data analysis. The most commonapplications concerns surveys, literary texts and speeches analyses, and a highquantity of text is usually required (Lebart et al. 1998). Sensory applicationsalso exist. In the studies of Martin and Rogeaux (1994) and of Ten Kleij and

373PRODUCT SPACE FROM ULTRA-FLASH PROFILING


3/24

Musters (2003), the maps obtained, from 111 and 165 consumers, respectively,were interpretable and allowed a rough description of the products. The textualanalysis of 522 free-text comments from a gastronomic and wine guide alsogave good results (Becue-Bertaut et al. 2008).

In the context of sensory analysis, particularly when applied to wines, itis difcult to get descriptions as numerous as the ones mentioned previously.A usual assumption is that the high level of expertise of the wine profession-als and the existence of a common vocabulary could compensate for the lownumber of descriptions. From this perspective, Sauvageot et al. (2006) ana-lyzed free descriptions of 12 wines from nine experts, but they could notvalidate the method because of a great variability between the experts,already highlighted by Brochet and Dubourdieu (2001). However, it can beassumed that descriptions collected in the context of a holistic sensory task(i.e., free-sorting task or Napping®) could be more consistent than a totallyfree-text description. Indeed, the sorting/positioning task could familiarizethe judges with the product space studied. Moreover, with the same under-lying assumption than in the Flash prole method (Dairou and Sieffermann2002; Delarue and Sieffermann 2004), a sensory task based on a comparisoncould incite the judges to focus on the perceived differences and to usediscriminating attributes. In the Flash prole method, the description isobtained from a ranking of the products, attribute per attribute. The holisticmethods, such as free-sorting task or Napping®, are also based on a simul-taneous presentation. However, the descriptions arising from these methodsare usually considered only as a complement of the conguration obtainedfrom the main sensory task, and are introduced as illustrative variables in themain analysis (usually multidimensional scaling for free-sorting data andmultiple factor analysis (MFA) for Napping® data) (Pagès 2003; Faye et al.2004; Soufet et al. 2004; Cartier et al. 2006; Blancher et al. 2007; Perrinet al. 2008).

In such context, we proposed to use UFP for wine description, with alimited number of experts. Based on an example applied to 10 red wines fromthe Central Loire Valley region in France, the aim of the present study was,rst, to evaluate the feasibility of this proposed method by checking if thenumber of words per wine was sufcient for a statistical analysis. Then, theability of UFP to provide interpretable maps, produced by a limited number of wine experts and analyzed without considering the Napping® positioning, wasalso evaluated. Moreover, the results were compared with the results obtainedfrom classical proling, performed independently by another jury of wineexperts, in order to evaluate the respective contribution of the two approaches.Finally, beyond the example, some methodological choices are discussed, suchas the minimal quotation frequency from which the items were introduced inthe analysis.

374 L. PERRIN and J. PAGÈS


4/24

MATERIAL AND METHODS

Samples

The product space was composed of 10 red wines from the Anjou Rougedesignation and from the Anjou Village Brissac designation. Wines came fromve different producers (coded 8, 33, 38, 43 and 55), producing a wine fromeachdesignation and fromvintage 2005. Bottles werestoredat11C.Wineswereserved at room temperature inclear InstitutNationaldesAppellationsd’Origine(INAO)wineglasses(NFV09-1101971)labeledwithrandomthree-digitcodesand covered with plastic Petri dishes. Wines were veried free of cork taint bythe panel leader. Evaluations were performed in June 2007.Napping® Positioning

Two sessions of Napping® positioning, with a week in between, werecarried out by 14 wine professionals from the Brissac area (Loire Valley,France). At each session, the 10 wines were simultaneously presented to each judge. Judges were requested to layout the products on a paper tablecloth(40 cm ¥ 60 cm) in such a way that two wines were very near if they seemedidentical and that two wines were distant from one another if they seemeddifferent.

UFP

Sensory Procedure. After each Napping® positioning, the 14 profes-sionals carried out an UFP. They were asked to enrich their Napping® table-cloth by adding terms directly on the sheet to describe the wines.

Encoding Terms. Judges were free to use terms as they like and to usequantiers (i.e., “very,” “slightly,” “not” or “without”) with the terms. Whenquantiers were used, a choice was made to take them into account by con-sidering the combination (quantier + attribute) as a single and exclusive item(for example, “very_fruity ,” “not_acid ,” “without_expression ,” etc.). More-over, when an adjective completed a name, they were also considered as anitem (for example, “balanced_tannins ,” “rough_tannins ,” “slight_structure ,”etc.).

An occurrence matrix (wines ¥ items) was built, with the general term xij = number of times that the item i was cited for the wine j, whatever the judges and the sessions. No regrouping was performed since it was not surethat several items with an a priori similar meaning (for example, “blackcur-rant ” with “black_fruit ” or “vegetal ” with “green ”) referred exactly to thesame sensation.



5/24

Conventional Prole

Eight wine professionals from Loire Valley, different from the previous14 professionals, carried out a conventional prole, with two replications.Unlike the panels classically used in sensory analysis, this panel was notformally trained. However, they were highly experienced; professionals havegreat knowledge of wine and are used to taste them. The attribute list(Table 1) was established by the panel leader on the basis of the usualattributes used on red wines from Loire Valley and commented at the begin-ning of the tasting session. Experts carried out the two replications in thesame session, with a 20-min break in between. For each replication, the 10wines were presented in a sequential monadic way and according to ordersbased on a William Latin-square arrangement, with the same arrangementfor each judge among the two replications. Each assessor scored the winesfor each term. A time delay of 120 s between samples was applied thanks tothe software. Assessors scored the wines on unstructured linear scales,anchored on the left end with “low” intensity and on the right end with“high” intensity, as usually adopted in the laboratory (Jourjon et al. 2005).They marked each value on the scale. Scores were collected by FIZZ(version 2.10; Biosystems, Courtenon, France), converted to scores from 0 to10 and exported to an Excel spreadsheet.

TABLE 1.LIST OF ATTRIBUTES SCORED IN THE CONVENTIONAL PROFILE

Color intensity From light to darkBrightness From mat to brightOaky odor intensity From low to highSpicy odor intensity From low to high

Black fruits odor intensity (blackcurrant, blackberries, cherry) From low to highRed fruits odor intensity (redcurrant, strawberry, raspberry) From low to highCandied/jam odor intensity From low to highViolet odor intensity From low to highFlowery (except violet) odor intensity From low to highAnimal odor intensity From low to highGreen bell pepper odor intensity From low to highCut grass odor intensity From low to highMushroom odor intensity From low to highAromatic persistency From low to highAcidity From low to high

Alcohol From low to highFull body From low to highMouth thickness From uid to thickAstringency From low to highBitterness From low to high



6/24

Data Analyses

Data analyses were performed using SPAD software (SpadVersion: MN:6.0.1, Paris, France), SensoMineR package (Husson and Lê 2007), Facto-MineR package (Husson et al. 2007) and Stagraphics Plus software (version5.1, Statistical Graphics Corp., Sigma Plus, Toulouse, France).

Base Statistics. Some statistical indicators were selected in order evalu-ate the use of the method by the wine experts. Thus, the items used per wine,per judge, per description (i.e., a description corresponding to a list of itemsused for a wine on a tablecloth), etc., were calculated. In addition, to evaluatethe variability of the number of items generated by the judges or describing thewines, the variable number of items per description was studied, thanks to anANOVA according to the following model:

number of items wine judge wine judge the judge effect wa

= + + × +ε ss considered as random( )

Threshold Selection. CA is the reference method for the analysis of acontingency table (Lebart et al. 1998). However, because of the high numberof items used at a low frequency, the global contingency table had rst to bereduced and simplied in order to keep a statistical sense (Lebart et al.1998). The threshold is usually arbitrarily determined by the experimenter(Martin and Rogeaux 1994; Faye et al. 2004; Soufet et al. 2004; Cartieret al. 2006; Blancher et al. 2007) since there is no formal rule. In the presentexperiment, several global matrices, different one from another according totheir threshold, were thus confronted to evaluate the inuence of the thresh-old chosen on the results. Six different thresholds from a quotation fre-quency higher or equal to 1 to a quotation frequency higher or equal to 6were studied. Instead of comparing the matrices directly, the results of theanalyses of the matrices were compared by studying the rst two factors of the CA. The rst two axis of each CA were thus introduced as six unstand-ardized groups in an MFA (Pagès and Husson 2001; Escoer and Pagès1998) in order to compare them in the same subspace. However, an issueabout the wine weights appeared. In each CA, wines (rows) were assignedwith a weight depending on the number of terms received for the consideredmatrix (i.e., the six matrices being different, the weights were different fromone matrix to another); this weight constituted the marginal column prole.In this MFA, a choice was made to assign to the wines a weight based on thetotal number of items that the wine received, without any reduction of thematrix (i.e., threshold xed at 1). The data used in the MFA were thus, notexactly the same as that of the original rst two factors of the six CA.



7/24

However, because of similar matrix structures, the marginal column prolesof the matrices were expected to be similar. In order to check if the differ-ences were small, the eigenvalues of the original CA and the separate prin-cipal component analysis (PCA) performed on the rst two axis of the CA(in the MFA) were thus compared.

In addition, the global similarity between the results of the six CA wasmeasured through the regression vector (RV) coefcients (Escouer 1973)between the six sets, including the two rst axes of each CA. It allowed aglobal comparison of the six CA, restricted to their rst two factors. Moreover,in order to choose the matrix closest to the whole matrices, RV coefcientsbetween the six couples of axes of each CA and the global compromise of theMFA were also computed.

Wine Characterization. Characterization from UFP. Once the thresh-old was selected, a CA was performed on the appropriate contingency table(wines ¥ items).

Comparison of the Characterizations from UFP and from ConventionalProling (CP). The data from CP were classically analyzed thanks toANOVAs according to the model attribute = wine + judge + wine ¥ judge + e(the judge effect being considered as random), and thanks to a PCA performedon the discriminating attributes.

In order to compare the product space map obtained from the UFP withthe one obtained from the conventional proling, the rst two axes of the PCAwere introduced as illustrative variables in the previous CA. Besides, in orderto compare the two descriptions, attributes from conventional proling werealso introduced as illustrative variables in the CA. The illustrative variables didnot participate in the axis construction, but each correlation coefcient withthe CA factor was calculated and represented. Finally, a RV coefcient wasalso computed between the rst two axes of the CAand of the PCA. The winescould thus be assigned with the weight resulting from the CA or from the PCA(in this case, the 10 wines had the same weight).After verifying that the weightassigned to the wines did not almost modify the results, for more simplicity,a choice was made to give the same weight to the 10 wines.

RESULTS AND DISCUSSION

General Statistics about the Use of Terms

In order to describe the wines during the two sessions, 293 items wereused, with 141 used only once (by a judge, for a given wine and during one



8/24

of the two sessions). In spite of a low quotation frequency, these items werenot useless. However, they were not statistically usable and they were notintroduced in the CA. Besides, 200 items were used by only one judge(Fig. 1), which means that about a third of the items were shared by at leasttwo judges.

Figure 2 presents the 20 terms that are used the most by the whole juryduring the two sessions. Among these 20 items, six referred to fruity notes

( fruity , black fruits , red fruits , blackcurrant , ripen fruits and without fruit ).Theother items referred to other aromatic notes (vegetal , animal , Brettanomyces 1,dust ), to color, and to taste and sensations. In order to describe these sensa-tions, wine professionals used many items (structured , smooth , balanced ,round , rich , complex , etc.). However, it can be assumed that the denitionabout them is not completely clear and shared. Moreover, some of these itemscould have hedonic connotations (for example, balanced , rich or complex ).This marks some of the differences that exist between a spontaneous descrip-tion and a conventional characterization, in which hedonic terms should be

avoided (ISO 11035 1994; ISO 13299 2003).1 Brettanomyces is the name of a yeast that transforms the cinnamic acids in volatile phenols, among

which the ethyl-4-phénol with the characteristic odour of “horse stables” (Ribéreau-Gayon et al.1998).

Number of items

200

48

199

3 2 2 6 2 1 1 0 0 00

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Number of judges

200

48

199

3 2 2 6 2 1 1 0 0 00

50

100

150

200

250

1 2 3 4 5 6 7 8 9 10 11 12 13 14

FIG. 1. USING FREQUENCY OF THE ITEMS200 items were used by only one judge whereas 0 items were used by all 14 judges.



9/24

Three descriptions (“description” as a couple wine ¥ tablecloth) did notcontain any item (Fig. 3). However, about 85% of the descriptions were con-stituted by at least two items; in most of cases, the judges were not contenton giving one word per wine, but instead gave a full description. On thecontrary, only six descriptions among the 280 possible ones (10 wines ¥ 14 judges ¥ 2 sessions), were composed by at least 10 items. This variabilitycould be linked to the judges and/or to the wines. For example, wine 8AR1received the lowest number of items (97) whereas wine 43AVB1 receivedthe highest (123) (Fig. 4). Yet, the ANOVA indicated that the differencebetween the wines was not signicant (P = 0.1633). This result is not con-sistent with the hypothesis of Becue-Bertaut et al. (2008) that some wines,for example the preferred wines, received more items that the others. Theinteraction wine ¥ judge was not signicant either (P = 0.9980). However,signicant differences were observed between the 14 judges (P = 0.0000);the judge that used the lowest number of items used 10 and 11 items, for therst and the second session, respectively, whereas the judge that used thehighest number of items used 99 and 51 items, respectively. Hence, in thisexample, variability arose mainly from the judges.

At least 10 terms were used per tablecloth in order to describe the 10wines (Fig. 5). The highest number of items received per tablecloth wasbetween 60 and 70 items and concerned 25% of the tablecloths. Twenty-one(21%) of the tablecloths received 20 to 30 items, and 18% received 10 to 20items. One tablecloth received 99 terms to describe the 10 wines.

48

44 43

38

3432

24 2320 19 19 18 17

13 13 12 11 10 9 9

0

10

20

30

40

50

60

f r u

i t y

s t r u c

t u r e

d

b l a c

k f r u

i t s

r e d f r u

i t s

v e g e

t a l

d r y

t a n n

i n s

a s

t r i n g e n

t

s m o o

t h

b a

l a n c e

d

b l a c

k c u r r a n

t

r o u n

d

r i p e n

f r u

i t s

a n

i m a

l

a c

i d r i c

h

c o m p

l e x

B r e

t t a n o m y c e s

s t r o n g c o

l o u r

d u s

t

w i t h o u

t f r u

i t

Number of citations

48

44 43

38

3432

24 2320 19 19 18 17

13 13 12 11 10 9 9

0

10

20

30

40

50

60

f r u

i t y

s t r u c

t u r e

d

b l a c

k f r u

i t s

r e d f r u

i t s

v e g e

t a l

d r y

t a n n

i n s

a s

t r i n g e n

t

s m o o

t h

b a

l a n c e

d

b l a c

k c u r r a n

t

r o u n

d

r i p e n

f r u

i t s

a n

i m a

l

a c

i d r i c

h

c o m p

l e x

B r e

t t a n o m y c e s

s t r o n g c o

l o u r

d u s

t

w i t h o u

t f r u

i t

FIG. 2. MOST CITED ITEMS



10/24

a

Number of descriptions

3

3950

3960

3322

12 9 7 2 2 20

50

100

150

200

250

Number of items per description

3

3950

3960

3322

12 9 7 2 2 20

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9 10 11 120 1 2 3 4 5 6 7 8 9 10 11 12

3

3950

3960

3322

12 9 7 2 2 20

50

100

150

200

250

bCumulative number of descriptions

24613

2234

56

89

149

188

238

277

0

50

100

150

200

250

300

Number of items per description

24613

2234

56

89

149

188

238

277

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12≥ 1 ≥ 2 ≥ 3 ≥ 4 ≥ 5 ≥ 6 ≥ 7 ≥ 8 ≥ 9 ≥ 10 ≥ 11 ≥ 12

FIG. 3. NUMBER OF ITEMS PER DESCRIPTION: (A) DISTRIBUTION; AND (B)CUMULATIVE DISTRIBUTION



11/24

aNumber of items per wine

110 112 114123

10197104 105 105 107

0

20

40

60

80

100

120

140

8 A R 1

5 5 A V B 1

3 3 A V B 2

3 3 A R 1

5 5 A R 1

8 A V B 1

3 8 A V B 1

4 3 A R 1

3 8 A R 1

4 3 A V B 1

mean: 107.8

110 112 114123

10197104 105 105 107

0

20

40

60

80

100

120

140

8 A R 1

5 5 A V B 1

3 3 A V B 2

3 3 A R 1

5 5 A R 1

8 A V B 1

3 8 A V B 1

4 3 A R 1

3 8 A R 1

4 3 A V B 1

mean: 107.8

bNumber of items per judge

10,5 13,519

25 26

38

26,5

7567,5

53,55250

43,539

0

20

40

60

80

100

120

J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 J13 J14

mean: 38.8

10,5 13,519

25 26

38

26,5

7567,5

53,55250

43,539

0

20

40

60

80

100

120

J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 J13 J14

10,5 13,519

25 26

38

26,5

7567,5

53,55250

43,539

0

20

40

60

80

100

120

J1 J2 J3 J4 J5 J6 J7 J8 J9 J10 J11 J12 J13 J14

mean: 38.8

FIG. 4. NUMBER OF ITEMS USED FOR (A) EACH WINE; AND (B) BY EACH JUDGE



12/24

a

0

5 6

3 4 2

7

0 01

0

5 6

3 4 2

7

0 01

Number of tablecloths

0

5 6

3 4 2

7

0 01

0

5

10

15

20

25

0 - 1

0

1 0 - 2

0

2 0 - 3

0

3 0 - 4

0

4 0 - 5

0

5 0 - 6

0

6 0 - 7

0

7 0 - 8

0

8 0 - 9

0

9 0 - 1

0 0

Number of items per tablecloth

b

≥ ≥ ≥ ≥ ≥ ≥ ≥ ≥

Number of items per tablecloth

111

810

14

17

23

28

Cumulative number of tablecloths

0

5

10

15

20

25

30

≥

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

111

810

14

17

23

28

111

810

14

17

23

28

FIG. 5. NUMBER OF ITEMS PER TABLECLOTH: (A) DISTRIBUTION; AND (B)CUMULATIVE DISTRIBUTION



13/24

More generally, the base statistics show that a high number of items weregenerated after the Napping® positioning.

Threshold Selection

The Fig. 6 compares the eigenvalues of the six original CA with theeigenvalues of the six separate PCA performed on the rst two axis of the CAin the MFA. All points were very close to the line corresponding to theequation y = x ; the eigenvalues of the CA were similar to those of the separatePCA. The weight assigned in the MFA was thus adequate and did not modifythe results in relation to the original analyses.

Figure 7 represents the link between the rst two factors of the sixdifferent CAand the MFA. The rst factors of each CAwere globally close oneto the other and to the rst factor of the MFA. It was also the case for thesecond factors. These results indicated a good stability between the differentCA. Whatever the threshold chosen, the results obtained from the CA weresimilar. In this example, the threshold was not decisive. The highest RVcoefcient with the compromise was observed for the CA realized with itemshaving a quotation frequency higher or equal to 3 (Table 2). Moreover, when

0.075 0.150 0.225 0.300 0.375

0.075

0.150

0.225

0.300

0.375

0

λ 1

1

λ 2

1

λ 1

6

λ 1

5

λ 2

5

λ 2

4

λ 1

4

λ 1

3

λ 1

2

λ 2

3 λ 2

2

λ CA

λ MFA

y = x

λ 2

6

0.075 0.150 0.225 0.300 0.375

0.075

0.150

0.225

0.300

0.375

0

λ 1

1

λ 2

1

λ 1

6

λ 1

5

λ 2

5

λ 2

4

λ 1

4

λ 1

3

λ 1

2

λ 2

3 λ 2

2

λ CA

λ MFA

y = x

λ 2

6

FIG. 6. EIGENVALUES (l 1 AND l 2) OF THE CA AND OF THE SEPARATE PCA IN THE MFA(THRESHOLD FROM 1 TO 6)



14/24

Factor 2 - 45.91 %

65

4

4

3

3

2

2

1

65

1

Factor 1 - 50.11 %

65

4

4

3

3

2

2

1

65

1

FIG. 7. REPRESENTATION OF THE PARTIAL AXES OF THE SIX (THRESHOLDS FROM 1TO 6) SEPARATE ANALYSES (PCA) IN THE MFA (MFA – FACTOR 1–2)

The full arrows correspond to the rst factors; the dotted arrows correspond to the second factors.

TABLE 2.RV COEFFICIENT BETWEEN THE TWO FIRST AXES OF THE SEPARATE ANALYSES

(PCA) AND THE MFA COMPROMISE

RVcoefcients

Threshold= 6

Threshold= 5

Threshold= 4

Threshold= 3

Threshold= 2

Threshold= 1

Threshold = 6 1.0000Threshold = 5 0.9963 1.0000Threshold = 4 0.9173 0.9067 1.0000Threshold = 3 0.9251 0.9113 0.9589 1.0000Threshold = 2 0.9004 0.8859 0.9568 0.9904 1.0000Threshold = 1 0.8196 0.8129 0.8094 0.9142 0.9092 1.0000Compromise 0.9641 0.9563 0.9620 0.9888 0.9788 0.9144



15/24

the threshold was xed to 3, the coordinates of the CA were best representedin the global MFA and the closest to the compromise (Fig. 8). The thresholdwas thus xed at a quotation frequency higher or equal to 3.

Wine Characterization

Characterization from UFP. Results from the CA. The CA was real-ized on the contingency table, with items having a quotation frequencyhigher or equal to 3; 107 items were thus introduced in the CA. Thesequence of the eigenvalues (Table 3) suggested keeping the rst two factors;they accounted for about 39% of the total inertia. This percentage of inertiawas close to those found in similar studies (Chollet and Valentin 2000),(Soufet et al. 2004).

The rst factor of the CA (Fig. 9) mainly opposed the wines from theproducer coded 8 (wines 8AR1 and 8AVB1) to the others, particularly to wine

6

5

4

3

21

1.000.750.500.2500

0.25

0.50

0.75

1.00

Factor1 - 50.11 %

Factor2 - 45.91 %

6

5

4

3

21MFA

FIG. 8. REPRESENTATION OF THE SIX GROUPS (THRESHOLDS FROM 1 TO 6) IN THEMFAAND OF THE MFA COMPROMISE (MFA – FACTOR 1–2)



16/24

43AVB1. This rst factor was mainly dened, on the 43AVB1 side, by theitems nice matter , tannins , oaky , drying tannins , intense color , fat , nice struc-ture , intense tannins , astringent and alcohol , which were opposed to black-currant bud , green bell pepper , very fruity , owery , blackcurrant , simple andblackberry . This rst factor opposed thus “structured” and “strong” wines to“lighter” wines with fruity and vegetal notes (blackcurrant bud and green bell pepper ).

TABLE 3.EIGENVALUES OF THE CA (THRESHOLD = 3)

Factors 1 2 3 4 5

Eigenvalues 0.2576 0.2268 0.1653 0.1405 0.1201Percentages 20.87% 18.38% 13.40% 11.38% 9.73%Cumulative percentages 20.87% 39.24% 52.64% 64.02% 73.75%

Factor 1 - 20.87 %

33AR1

33AVB2

38AR1

38AVB143AR1vegetal

astringent

smooth

blackcurrant

animal

Brettanomyces

strong

dirty

fatlight

not clean

silky tannins

slight astringency

nice structure

blackcurrant bud

to be kept

fresh

candied fruits

low aromatic intensity

weak

surprising nose

green bell pepper

simple

tannins

firm tannins

intense tannins

very fruity

liquoricecandy

pleasant mouth

oaky

butyric

garnet colour black colour

bright colour

flowery

straight

high maturity

insufficient maturity

blackberry

fresh nose

intense nose

phenol nose

low structure

coated tannins

very tannic

vivid

43AVB1

55AR1

55AVB1

8AR1

8AVB1

nice matter

lactic

alcohol

not round

full

intense colour

drying tannins

0.750-0.75-1.50

-1.50

-0.75

0

0.75

Factor 2 - 18.38 %

33AR1

33AVB2

38AR1

38AVB143AR1vegetal

astringent

smooth

blackcurrant

animal

Brettanomyces

strong

dirty

fatlight

not clean

silky tannins

slight astringency

nice structure

blackcurrant bud

to be kept

fresh

candied fruits

low aromatic intensity

weak

surprising nose

green bell pepper

simple

tannins

firm tannins

intense tannins

very fruity

liquoricecandy

pleasant mouth

oaky

butyric

garnet colour black colour

bright colour

flowery

straight

high maturity

insufficient maturity

blackberry

fresh nose

intense nose

phenol nose

low structure

coated tannins

very tannic

vivid

43AVB1

55AR1

55AVB1

8AR1

8AVB1

nice matter

lactic

alcohol

not round

full

intense colour

drying tanninsnice matter

lactic

alcohol

not round

full

intense colour

drying tannins

FIG. 9. CA PERFORMED ON THE UFP DATA, WITH A QUOTATION FREQUENCYTHRESHOLD FIXED AT 3 (F1–F2)

The symbol size corresponds to the weight of the wines and of the items (the bigger a symbol is,the higher is the weight).



17/24

The second factor opposed the wines from the producer coded 55 (wines55AR1 and 55AVB1) and wine 8AR1 to the other wines, particularly to wine8AVB1. This factor opposed items such as phenol nose , Brettanomyces ,butyric , not clear , dirty and animal , but also items such as bright color , lowaromatic intensity , low structure and slight astringency to items such as nicematter , intense nose , “to be kept ” and to aromatic notes such as blackcurrant bud , owery , surprising nose , very fruity , candied fruits and licorice candy .This second factor highlighted a “strength” dimension, but also a sensorycomponent that could refer to an “organoleptic anomaly” characterizing thewines from the producer 55.

More generally, the UFP data highlighted a “strength” dimension (seconddiagonal of the CA).

Quotation Frequency of the Terms and Ability to Discriminate theWines. In order to evaluate the discriminating ability of the items, the quota-tion frequency of the 20 most cited ones were compared with their distancefrom the origin (d2) in the CA. Among the 20 most cited items, only 10contributed highly to the axes formation (Table 4). The 10 other items,

TABLE 4.RELATIVE WEIGHT, CONTRIBUTION TO THE CA (d2) ANDCONTRIBUTION TO THE FIRST TWO FACTORS OF THE CA

OF THE 20 MOST CITED ITEMS

Item Relative weight d2 Factor 1 Factor 2

Fruity 5.65 0.15 0.30 0.38Structured 5.18 0.12 0.36 0.90Black fruits 5.06 0.16 1.56 0.93Red fruits 4.47 0.10 0.32 0.52

Vegetal 4.00 0.69 5.16 0.19Dry tannins 3.76 0.55 0.02 0.43Astringent 2.82 0.83 5.24 1.47Smooth 2.71 1.10 2.59 4.13Balanced 2.35 0.25 0.57 0.11Blackcurrant 2.24 2.49 13.93 1.99Round 2.24 0.33 0.08 0.69Ripen fruits 2.12 0.44 1.80 0.51Animal 2.00 1.61 1.29 7.80Acid 1.53 1.42 0.34 0.16Rich 1.53 0.39 0.50 1.00

Complex 1.41 1.22 1.28 0.24 Brettanomyces 1.29 2.85 0.49 12.07Strong color 1.18 0.01 0.00 0.01Dust 1.06 0.61 0.37 0.03Without fruit 1.06 0.83 0.07 0.39



18/24

although cited more than nine times, did not allow to differentiate the wines.These items were fruity (cited 48 times), structured (44 times), red fruits(38 times), dry tannins (32 times), balanced (20 times), acid (13 times), rich(13 times), strong color (10 times), dust (9 times) and without fruit (9 times).In spite of high quotation frequency, the judges did not use these items todescribe the same wines, but this did not question the individual discrimina-tion. It could indeed be attributed to a lack of denition and/or of commonreferences. On the contrary, the items “strong color” and “without fruit” wereboth used by a unique judge to describe 10 and 9 wines, respectively, whichdid not allow discrimination among the wines.

Comparison of the Characterizations from UFP and from ConventionalProling. The rst two factors of the PCA, computed on the conventionalprole data, were linked to the rst two factors of the CA (Fig. 10a). TheRV coefcient (RV = 0.6697) between the rst planes was signicant(P = 0.0014); the two congurations were not independent. However, thefactors were inverted. The correlation coefcient between the rst factor of the conventional prole and the second factor of the UFP was 0.78 and thecorrelation coefcient between the second factor of the conventional proleand the rst factor of the UFP was 0.82. In the UFP, inertias were equivalentfor the rst two, which would explain the inverted correspondence with con-ventional prole.

In addition to the wine maps, the descriptions from the UFP globallycoincided with the ones from the CP (Fig. 10b). In the CP, a “strength dimen-sion,” a pole of fruity descriptors and a pole of the aromatic notes usuallyqualied in the wine profession as “austere” (mushroom and animal ) were alsovisible.

The items black fruit , astringent and animal , common both to the CP andto the UFP, allowed to differentiate the wines, whereas acid did not allow anydiscrimination, neither imposed (CP) nor spontaneously generated (UFP).Interestingly, for a same idea the judges were more severe in the free descrip-tion; the wines characterized with mushroom and animal notes in the CP weredescribed with the strong items not clear , dirty , Brettanomyces , etc.

In spite of great similarities, some differences could yet be underlined. Inthe CP, the vegetal note was separated in green bell pepper and cut grass notes. These two attributes were independent (r = 0.0087). In the spon-taneous generation, the item vegetal was the most used and a satisfyingconsensus was observedbetween the judges inorder tohighlight the differencesamong the 10 wines. However, the attributes green bell pepper and cut grasswere not very correlated to the CA dimensions. The green bell pepper item wasused differently between the imposed description and the free description.Finally, some discriminative attributes in the CP, such as violet , were not



19/24

b Factor 2 - 18.38 %

Colour intensity

Oaky

Black fruits

Red fruits Candied

Violet

Flowery

Animal

Green bellpepper

Cut grass

Mushroom

Persistency

Alcohol

Thickness Astringency

Factor 1 - 20.87 %

Colour intensity

Oaky

Black fruits

Red fruits Candied

Violet

Flowery

Animal

Green bellpepper

Cut grass

Mushroom

Persistency

Alcohol

Thickness Astringency

a

F2 PCA CP (41%)

F2 PCA CP (26%)

Factor 1 - 20.87 %

Factor 2 - 18.38 %

F2 PCA CP (41%)

F2 PCA CP (26%)

FIG. 10. REPRESENTATION OF THE FIRST TWO FACTORS OF THE PCA PERFORMED ON(A) CONVENTIONAL PROFILE DATA; AND (B) OF THE ATTRIBUTES, INTRODUCED AS

ILLUSTRATIVE VARIABLES IN THE CA ON THE UFP DATA (F1–F2)



20/24

spontaneously generated. On the contrary, the attribute red fruits were notdiscriminative in CP but allowed to differentiate the wines in the UFP.

CONCLUSION

In spite of the small number of descriptions per product (wines evaluatedtwice by 14 judges), many items were generated for every wine (with a totalfrom 97 to 123 items per wine with and a mean of 3.85 items per wine).Among those, about a third was shared by at least two judges. This importantgeneration of items could mainly be explained by the expertise; wine profes-sionals are used to describe the wines and use a normative vocabulary. Inaddition, the wine map issued from the UFP was interpretable and the resultswere robust whatever the threshold chosen. The main dimensions of the winemap were also corroborating with the ones obtained from a conventionalprole, performed independently by another jury of wine professionals. Thewine characterization from UFP is expected to be less precise than one that aconventional prole could provide. Indeed, in the conventional approach, allattributes are scored for each wine and refer to denitions. However, theinformation arising here from UFP was more complete; at the individual level, judges are incited to select discriminating items and the free descriptionallowed here to generate unexpected items. For example, the characteristics“animal” and “mushroom” of wines 55AR1 and 55AVB1 were in fact per-ceived by the wine professionals as a aw attributable to the Brettanomycesyeast. Finally, since each item was specically chosen for the characterizationof a wine (or sometimes, a group of wines), the UFP method limited theappearance of correlations between attributes, which is frequently observedwith the conventional proling method.

Both the interpretability of the map obtained from UFP and the goodcorroborating of maps and descriptions with conventional proling indicatedthat the data from the Ultra-ash prole could be self-sufcient. These resultssubstantiated our two assumptions and showed that it was possible to getinteresting results from a limited number of free descriptions per product (14 judges, two evaluations). The vocabulary was sufciently shared by the wineprofessionals to get a stable characterization of the wines, and the Napping®positioning conducted with or previously to free description incited the judgesto generate discriminating items.

ACKNOWLEDGMENT

This work was supported by Interloire and the Union des Oenologuesand was conduced at the laboratory Grappe from the Ecole Supérieure



21/24

d’Agriculture of Angers. We would like to thank Frédérique Jourjon, RonanSymoneaux and Isabelle Maître for their remarks and the Unité Vigne et Vinof the INRA of Angers for the data used in the wine selection and for theirassistance during the experiment. Special thanks are due to the wine profes-sionals for the tasting sessions. Finally, we would like to acknowledge MurielColin-Henrion for the revision of the English version of the manuscript.

REFERENCES

BECUE-BERTAUT, M., ALVAREZ-ESTEBAN, R. and PAGES, J. 2008.Rating of products through scores and free-text assertions: Comparingand combining both. Food Qual. Prefer. 19, 122–134.

BLANCHER, G., CHOLLET, S., KESTELOOT, R., HOANG, D.N., CUVE-LIER, G. and SIEFFERMANN, J.-M. 2007. French and Vietnamese:How do they describe texture characteristics of the same food? A casestudy with jellies. Food Qual. Prefer. 18, 560–575.

BROCHET, F. and DUBOURDIEU, D. 2001. Wine descriptive languagesupports cognitive specicity of chemical senses. Brain Lang. 77 , 187–196.

CARTIER, R., RYTZ, A., LECOMTE, A., POBLETE, F., KRYSTLIK, J.,BELIN, E. and MARTIN, N. 2006. Sorting procedure as an alternative toquantitative descriptive analysis to obtain a product sensory map. FoodQual. Prefer. Sixth Rose Marie Pangborn Sensory Science Symposium17 (7–8), 562–571.

CHAMBERS, D.H., ALLISON, A.-M.A. and CHAMBERS, E. 2004. Train-ing effects on performance of descriptive panelists. J. Sensory Studies 19,486–499.

CHOLLET, S. and VALENTIN, D. 2000. Le degré d’expertise a-t-il uneinuence sur la perception olfactive? Quelques éléments de réponse dansle domaine du vin. L’Année Psychologique 100 , 11–36.

DAIROU, V. and SIEFFERMANN, J.-M. 2002. A comparison of 14 jamscharacterized by Conventional Prole and a quick original method, theFlash Prole. J. Food Sci. 67 , 826–834.

DELARUE, J. and SIEFFERMANN, J.-M. 2004. Sensory mapping usingFlash prole. Comparison with a conventional descriptive method for theevaluation of the avour of fruit dairy products. Food Qual. Prefer. 15,383–392.

ESCOFIER, B. 2003. Analyse des Correspondances: Recherches Au Coeur De L’analyse Des Données , Presses Universitaires de Rennes, Rennes,France.

ESCOFIER, B. and PAGÈS, J. 1998. Analyses Factorielles Simples et Mul-tiples , Dunod, Paris, France.



22/24

ESCOUFIER, Y. 1973. Le traitement des variables vectorielles. Biometrics 29,751–760.

FAYE, P., BRÉMAUD, D., DURAND DAUBIN, M., COURCOUX, P.,GIBOREAU, A. and NICOD, H. 2004. Perceptive free sorting and ver-balization tasks with naive subjects: An alternative to descriptive map-pings. Food Qual. Prefer. 15(7–8), 781–791.

GREENACRE, M.J. 1984. Theory and Applications of Correspondence Analysis , Academic Press, London, UK.

HEYMANN, H. 1994. A comparison of free-choice proling andmultidimensional scaling of vanilla samples. J. Sensory Studies 9, 445–453.

HUSSON, F. and LÊ, S. 2007. SensoMineR: Sensory data analysis with R. Rpackage version 107. http://sensominer.free.fr, http://www.agrocampus-rennes.fr/math/SensoMineR (accessed December 2007).

HUSSON, F., LÊ, S. and MAZET, J. 2007. FactoMineR: Factor analysis anddata mining with R. R package version 1.04. http://factominer.free.fr,http://www.agrocampus-rennes.fr/math/ (accessed December 2007).

ISO 11035. 1994. Analyse sensorielle – recherche et sélection de descripteurspour l’élaboration d’un prol sensoriel, par approche multidimension-nelle. In Recueil Normes Agroalimentaires, Analyse Sensorielle(AFNOR, ed.) pp. 517–546, AFNOR, Paris, France.

ISO 13299. 2003. Analyse sensorielle – méthodologie – directives généralespour l’établissement d’un prol sensoriel. In Recueil Normes Agroali-mentaires, Analyse Sensorielle (AFNOR, ed.) pp. 475–506, AFNOR,Paris, France.

JOURJON, F., SYMONEAUX, R., THIBAULT, C. and RÉVEILLÈRE, M.2005. Comparaison d’échelles de notation utilisées lors de l’évaluationsensorielle de vins. J. Int. Sci. Vigne. Vin. 39, 23–29.

LABBE, D., RYTZ, A. and HUGI, A. 2004. Training is a critical step to obtainreliable product proles in a real food industry context. Food Qual.Prefer. 15, 341–348.

LAWLESS, H.T. and HEYMANN, H. 1998. Descriptive analysis. In Sensory Evaluation of Food: Principles and Practices (C. Hall, ed.) pp. 341–378,Kluwer Academic / Plenum Publishers, New York.

LAWLESS, H.T., SHENG, N. and KNOOPS, S.S.C.P. 1995. Multidimen-sional scaling of sorting data applied to cheese perception. Food Qual.Prefer. 6 , 91–98.

LEBART, L., SALEM, A. and BERRY, L. 1998. Exploring Textual Data ,Kluwer Academic Publishers, Dordrecht/Boston/London.

MARTIN, N. and ROGEAUX, M. 1994. Etude par analyse textuelle decommentaires de consommateurs après dégustation de boissons. Sci.Aliments 14, 265–280.


http://www.agrocampus-rennes.fr/math/SensoMineRhttp://www.agrocampus-rennes.fr/math/SensoMineRhttp://factominer.free.fr/http://www.agrocampus-rennes.fr/mathhttp://www.agrocampus-rennes.fr/mathhttp://factominer.free.fr/http://www.agrocampus-rennes.fr/math/SensoMineRhttp://www.agrocampus-rennes.fr/math/SensoMineRhttp://www.agrocampus-rennes.fr/math/SensoMineRhttp://sensominer.free.fr/


23/24

NF V09-110. 1971. Equipment, Wine Tasting Glasses , June (AFNOR, ed.).AFNOR, Paris, France.

PAGÈS, J. 2003. Recueil direct de distances sensorielles: Application àl’évaluation de dix vins blancs du Val-de-Loire. Sci. Aliments 23, 679–688.

PAGÈS, J. 2005. Collection and analysis of perceived product inter-distancesusing multiple factor analysis: Application to the study of 10 white winesfrom the Loire Valley. Food Qual. Prefer. 16 , 642–649.

PAGÈS, J. and HUSSON, F. 2001. Inter-laboratory comparison of sensoryproles: Methodology and results. Food Qual. Prefer. 12(5–7), 297–309.

PERRIN, L., SYMONEAUX, R., MAÎTRE, I., ASSELIN, C., JOURJON, F.and PAGÈS, J. 2007. Comparison between a free proling carried out bywine professionals and a conventional proling. Am. J. Enol. Vitic. 58,508–517.

PERRIN, L., SYMONEAUX, R., MAÎTRE, I., ASSELIN, C., JOURJON, F.and PAGÈS, J. 2008. Comparison of three sensory methods for use withthe Napping(R) procedure: Case of ten wines from Loire valley. FoodQual. Prefer. 19, 1–11.

REINERT, M. 1986. Un logiciel d’analyse lexicale: ALCESTE. Cah. Anal.Donnees 4, 471–484.

RIBÉREAU-GAYON, P., DUBOURDIEU, D., DONÈCHE, B. andLONVAUD, A. 1998. Les prinicpaux défauts organoleptiques – chimiedu vin, stabilisation et traitements. In Traité d’Oenologie , pp. 277–293.Dunod, Paris.

SAINT-EVE, A., KORA, E.P. and MARTIN, N. 2004. Impact of the olfactoryquality and chemical complexity of the avouring agent on the texture of low fat stirred yogurts assessed by three different sensory methodologies.Food Qual. Prefer. 15(7–8), 665–668.

SAUVAGEOT, F., URDAPILLETA, I. and PEYRON, D. 2006. Within andbetween variations of textes elicited from nine wine experts. Food Qual.Prefer. 17 , 429–444.

SOUFFLET, I., CALONNIER, M. and DACREMONT, C. 2004. A compari-son between industrial experts’ and novices’ haptic perceptual organiza-tion: A tool to identify descriptors of the handle of fabrics. Food Qual.Prefer. 15(7–8), 689–699.

STONE, H., SIDEL, J., OLIVIERS, S., WOOSLEY, A. and SINGLETON,R.C. 1974. Sensory evaluation by quantitative descriptive analysis. FoodTechnol. 1974 , 24–28.

TANG, C. and HEYMANN, H. 2002. Multidimensional sorting, similarityscaling and free-choice proling of grape jellies. J. Sensory Studies 17 ,493–509.



24/24

TEN KLEIJ, F. and MUSTERS, P.A.D. 2003. Text analysis of open-endedsurvey responses:A complementary method to preference mapping. FoodQual. Prefer. 14, 43–52.

WILLIAMS, A.A. and LANGRON, S.P. 1984. The use of free-choiceproling for the evaluation of commercial ports. J. Sci. Food Agric. 35,558–568.

WOLTERS, C.J. and ALLCHURCH, E.M. 1994. Effect of training procedureon the performance of descriptive panels. Food Qual. Prefer. 5, 203–214.


creacion de un espacio con el metodo de ultra flash

Documents