cheeses sensory evaluation

International Dairy Journal 13 (2003) 6777

Comparison of free choice proling, direct similarity measurementsand hedonic data for ewes milk cheeses sensory evaluation

P. B!arcenas, F.J. P!erez Elortondo*, M. Albisu

Nutrici !on y Bromatolog!a, Facultad de Farmacia, Universidad del Pa!s Vasco (UPV/EHU), Paseo de la Universidad 7, 01006 Vitoria, Spain

Received 29 August 2001; accepted 30 July 2002

Abstract

The purpose of this work was to compare the sensory characteristics of ewes milk cheeses using several sensory methodologies

(free choice proling (FCP), direct similarity measurements, and hedonic data). For this purpose, visual plot inspection as well as

correlation coefcients among sensory dimensions were considered. Multidimensional maps obtained by means of free choice

proling proved difcult to interpret without any other external source of information in contrast to data obtained by direct

similarity measurement techniques that gave interpretable solutions. Cheese samples showed specic sensory characteristics mainly

based on ripening time and manufacturing procedures. The techniques used in this study revealed great individual variability during

sensory assessment of cheese samples mainly attributable to the absence of assessor training. Multidimensional solutions for the

different correlation techniques are also discussed.

r 2003 Elsevier Science Ltd. All rights reserved.

Keywords: Sensory properties; Ewes milk cheeses; Sensory methodologies

1. Introduction

Ewes milk cheeses have a unique taste and avour,very different from that made from cows milk(Kalantzopoulos, 1993). Furthermore, these minorityvarieties have a strong quality and authenticity imageand their traditional character plays a positive role onthe consumer market (Freitas & Malcata, 2000). Forthis reason, and considering a market dominated bycows milk products, the protection of traditional ewesmilk cheeses is taking on a new signicance (P!erezElortondo, B!arcenas, Casas, Salmer !on, & Albisu,1999b). Several research works have been recentlypublished on sensory characteristics within this groupof cheeses. A specic sensory lexicon and standardreferences have been previously described (Lavanchyet al., 1999; B!arcenas, P!erez Elortondo, Salmer !on, &Albisu, 1999). Futher studies were also carried out in theselection of descriptive panels for these types of products(B!arcenas, P!erez Elortondo, & Albisu, 2000; B!arcenas,P!erez Elortondo, Salmer !on, & Albisu, 2001a). More-

over, several studies have been focused on the effect ofdifferent factors, such as starter culture (Ortigosa,B!arcenas, Arizcun, P!erez Elortondo, Albisu, & Torre,1999) or ripening time (Ordo *nez, Iba *nez, Torre,Barcina, & P!erez Elortondo, 1998), has on ewes milkcheese sensory properties. However, all these studieshave been carried out using conventional sensoryproling techniques, and never by free choice proling(FCP) or any other alternative, and useful sensorymethodologies such as direct similarity measurements(DSM).Classical proling methods consider that each panelist

is using the sensory lexicon in the same way. However,several authors have stated that there are several sourcesof individual variation that cannot be completelyavoided even after intensive training (Williams &Arnold, 1985). In order to avoid these variabilitysources, FCP was considered as a possible solution,and it has been successfully used to describe differentcows milk cheeses such as Cheddar (Jack, Piggott, &Paterson, 1993) or Parmiggiano Regiano (Parolari,Virgili, Panari, & Zannoni, 1994). Although there areseveral statistical techniques available, most of theresearch work dealing with FCP employ GeneralizedProcrustes Analysis to treat the data (Dijksterhuis &

*Corresponding author. Tel.: +34-45-01-30-75; fax: +34-45-13-07-

56.

E-mail address: [email protected] (F.J.P. Elortondo).

0958-6946/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 5 8 - 6 9 4 6 ( 0 2 ) 0 0 1 3 9 - 5

Gower, 1991/2). Few applications have been describedon the use of other potentially useful techniques such asGeneralized Canonical Analysis (GCA) (van der Burg,Leeuw, & Dijksterhuis, 1994).On the other hand, sensory proling has received

some criticism. Some authors suggest that the groupingof individual sensory elements (sensory attributes) doesnot necessarily convey what is really being perceived,that is to say, that words cannot clearly describe what isbeing felt (Chauhan & Harper, 1986). In order to avoidthis negative point, other techniques such as DSM(Carroll & Chang, 1970) and hedonic measurements(B!arcenas, P!erez Elortondo, Salmer !on, & Albisu, 1998;B!arcenas, P!erez San Roman, P!erez Elortondo, &Albisu, 2001b) have been used to compare differentcheese varieties. Due to the existence of such a widerange of sensory methodologies, several procedures havebeen suggested in order to compare the results (King,Cliff, & Wall, 1998). A number of research studiesdealing with different sensory comparison methodolo-gies have pointed out that multidimensional map samplescore correlation analysis can be considered a useful toolin this approach (Heymann, 1994; Gilbert & Heymann,1995; Risvik, McEwan, & Rodbotten, 1997). As far asthe authors know, no research work has been publishedon ewes milk cheese sensory data comparison, and thetechniques used in this study.The objectives of this work were (1) to compare the

sensory characteristics of ewes milk cheeses using FCPand (2) to explore the differences and similaritiesbetween several sensory methodologies as well as diversestatistical multidimensional scaling techniques.

2. Materials and methods

2.1. Cheese samples

Eight different ewes milk cheese varieties wereconsidered for this study. Samples were selectedaccording to production and consumption in the areaunder investigation (Basque Country and NavarraNorth of Spain). Attention was focussed on theProtected Designation of Origin Idiazabal cheese, avariety made in the Basque Country and NavarraNorth of Spain from raw ewes milk which is veryimportant from the point of view of the agroindustryeconomy of this region (P!erez Elortondo et al.,1999a, b). The main sample technological characteristicsand cheese code numbers are presented in Table 1.

2.2. Assessors

The group of assessors was composed of 20 studentsfrom Facultad de Farmacia (VitoriaSpain), the numberof panelists being similar to the recommendations of

Piggott, Paterson, Fleming, and Sheen (1991/2) for thistype of study. They were all frequent consumers of ewesmilk cheeses. None had previous experience in sensoryanalysis, whether descriptive, discriminatory or hedonicmeasurements.

2.3. Sensory analysis

Three different sensory methodologies were employedin this study: FCP, DSM and preference measurements.Development of FCP was carried out in four steps.

1. Lexicon development. Kellys Repertory Grid Methodwas used to develop the list of descriptive terms thatpanelists used to evaluate the samples. Pairs ofsamples were presented to individuals and they wereasked to nd as many differences and similaritiesamong cheeses as possible. More details on thisprocess can be found elsewhere (B!arcenas, P!erezElortondo, Salmer !on, & Albisu, 1999). During thisstep, the objective of the study was explained to theassessors.

2. Individual sensory scale definition. Once the list ofterms had been developed, each term was located ona 7 point continuous scale marked with the anchors1=null or very slight to 7=very intense. In thisway, 20 individual scoresheets were dened, one foreach panelist.

3. Vocabulary selection. Assessors were given thecomplete set of samples to be studied thereafter.The objective of this session was twofold. First, todelete redundant or synonymous terms rst added tothe list, and at the same time to include any newdescriptors that had not been initially included in theballot. Second, to familiarize assessors with the sheetsthey would have to use when evaluating the samples.

Table 1

Main characteristics of ewes milk cheese samples under investigation

Code

number

Cheese variety Ripening

time

(months)

Characteristics

1 Idiaz!abal 3 Raw milk, non-

smoked, farmhouse

origin, PDOa


smoked, PDO

3 Idiaz!abal 3 Raw milk, smoked,

PDO


smoked, PDO

5 Idiaz!abal 7 Raw milk, smoked,

PDO

6 Roncal 10 Raw milk, PDO

7 Manchego 10 Pasteurized milk, PDO

8 Castellano 7 Raw milk

aPDO: Protected designation of origin cheese.

P. B !arcenas et al. / International Dairy Journal 13 (2003) 677768

4. Sample evaluation. Several portions of cheeses weregiven to each assessor of the different cheese varieties,to be tasted at home. They were wrapped in a plasticlm as when supplied from local supermarkets.Each cheese sample was coded with a randomnumber and to be consumed within 15 days of thedistribution date. It was also suggested that eachpanelist should store and consume the cheeses as theywould usually do. No other condition was imposed inthis instance.

DSM was carried out in normalized sensory booths,using the following procedure. Cheese samples wereassessed in terms of direct similarity using a 7 pointcategory scale (0=samples are the same; 6=samples arecompletely different). Similarity among all pairs ofsamples was evaluated, 4 pairs of samples per session, 2sessions per week. Using this methodology a group of 12matrices was obtained (one for each assessor) composedof 36 cheese similarity measurements, including acomparison between each sample with itself. Theexperimental design used is very similar to that usedby other researchers (Falahee & MacRae, 1995).The samples for the preference measurements were

assessed using a 7 point hedonic scale (1=dislikeextremely; 4=neither like nor dislike; 7=like extre-mely). The hedonic measurements were carried out athome using the samples given for FCP.

2.4. Statistical analyses

FCP data were submitted to GCA using OVERALSprogram (Gi, 1990). This program allows numericaland ordinal data treatment. Data were treated asnumerical and ordinal in order to compare results.Sensory terms were classied as odour, avour andtexture for analysis purposes. As suggested by van derBurg and Dijksterhuis (1996), sensory scores werereorganized into fewer new categories in order tominimize the existence of empty categories. Newcategories were dened as follows: category 1 (initialscores 1 and 2), category 2 (initial scores 3 and 4) andcategory 3 (initial scores 5, 6 and 7). In the case of odourattributes, category 1 was formed by initial scores 1 and2, category 2 by initial score 3 and category 3 by initialscores 4, 5, 6 and 7. Especially for odour, we decided toadopt this organization system in this way minimizingthe ocurrence of unique marginal frequencies (uniquepatterns). Unique patterns are correspondences betweensets shared by very few objects. For example, if there isonly one cheese sample made of goats milk and it is theonly one judged as animal odour, we have a uniquepattern. OVERALS algorithm is very sensitive to theselow marginal frequency categories obtaining extremelyhigh or low scores in the optimal scaling step (van derBurg et al., 1994). In order to interpret the dimensions

obtained, attributes with loadings of over 0.5 wereretained.As reported by van der Burg and Dijksterhuis (1996)

when carrying out OVERALS, if the measurement levelof all variables is numerical and there is only onevariable per set, then we are dealing with ordinaryPrincipal Component Analysis (PCA). In this case the tof a solution (the eigenvalue) corresponds to the meanexplained variance of the variables, as variables and setsare identical. This is not the case, however, when usingFCP data as in this study.In FCP, the results are evaluated by loss and tness

measurements. The loss shows the lack of t of asolution, being within a p-dimensional case, the mini-mum equal to 0 and maximum equal to p. Theeigenvalue can be calculated by dividing loss perdimensions, and carrying out 1 minus loss per dimen-sion. Eigenvalue is a goodness to t measure, and thesum of these values is called total t. Eigenvalues have amaximum value of 1.00 and the closeness to 1.00 is whatprovides an indication of goodness of t. Total t is thestatistical index widely used in GCA (OVERALS) todecide analysis solution dimensionality. There are clearmethods for determining the dimensionality when usingPCA (screen plot, explained variance of over 80%),whereas in the case of FCP the decision to establish thedimensionality is left in the hands of the panel leader.DSM data were submitted to Individual Difference

Scaling (INDSCAL) (Carroll & Chang, 1970). Thisstatistical treatment takes into consideration individualdifferences. The degree of t between nal congurationand original data is normally expressed by the stressmeasurement. Lower stress means a better t (Popper &Heymann, 1996). Another measurement of the degree oft is the squared correlation coefcient (RSQ) foundbetween the interpoint distances of the spatial cong-uration and the dissimilarities (original data). RSQranges from 0 (no t) to 1 (perfect t) and it is frequentlyinterpreted as the proportion of variance in the data thatis accounted for by the distances in the DSM model.Hedonic measurements were analyzed by means of

ANOVA and Taguchi signal-to-noise ratio (SNR)(Pastor, Costell, Izquierdo, & Dur!an, 1996). Theseprocedures take into consideration that acceptabilitycriteria are homogeneous. The variability of thepreference data was also considered and structureanalyzed using MDPREF. This technique is a metricmodel based on PCA (Eckart-Young decomposition) ona matrix of data, consisting of samples (objects) andconsumers (variables), grouping consumers according topreference criteria. The maps were obtained using theMDPREF Program of the PC-MDS MultidimensionalStatistic Package (Smith, 1990).Finally, Pearsons correlation coefcients were calcu-

lated among the sample scores obtained in the rst twodimensions from each type of multivariate analysis

P. B !arcenas et al. / International Dairy Journal 13 (2003) 6777 69

(OVERALS, INDSCAL and MDPREF). All statisticaltreatment, except MDPREF, was performed using SPSS6.1 Statistic Package (SPSS Inc., Chicago, USA).

3. Results and discussion

3.1. Free choice profiling (FCP)

The group of 20 assessors employed different sensoryterms to describe the odour, avour and texture of thecheese samples. The number of descriptors varied frombetween 10 and 23 for each panelist. Generally, odourwas the category in which the lowest number of termswas employed. Some of the terms previously identiedin cheese samples were deleted from the analysis as theyshowed a null variance value.

3.2. Odour

The bidimensional OVERALS solution for odourterms had eigenvalues of 0.836 and 0.636 for rst andsecond dimensions, respectively. Analysis total tnessfor odour FCP data was 1.472. As described by van derBurg and Dijksterhuis (1996), this level can beconsidered as adequate for this type of treatment. Fora two-dimensional solution, van der Burg and Dijk-sterhuis (1996) obtained different total t indexesdepending on the study: 1.64 (apples), 1.76 (meatproducts), 1.19 (water). OVERALS ordinal analysisrevealed the existence of three assessors with null losses.These results do not appear frequently, as they indicatethat the sensory scores obtained by this group of judgescan be adequately dened using the scores from the restof the panelists. However, in this case, it may be due tothe greater number of attributes used by the three judgesas well as the small amount of cheese samples used foranalysis.In order to restrict the analysis minimizing unique

patterns even more, data treatment was repeatedconsidering numeric levels. Results were very similarto those observed for ordinal levels: solution tness,eigenvalues obtained for each sensory term and cheesesample plots. For this reason, it was decided to analyzedata only from an ordinal point of view as suggested byvan der Burg and Dijksterhuis (1996).Fig. 1 represents the individual loss of each assessor as

well as the mean panel value for the bidimensionalOVERALS solution. It can be observed that assessornumber 6 is the one who worst ts the statistical modeldened by the rest of the individuals. The sum of loss inthe rst two dimensions for this panelist was 1.64. Forthis reason, his data were deleted from the analysis andthe measurements repeated in order to observe whetherthe solution was enhanced. However, the tness level

improved only slightly to 1.56, thus assessor data wereconsidered in further analysis.Fig. 2 shows the cheese sample plot. Smoked Idiaza-

bal cheese data for 3 months of ripening were deletedfrom the analysis as all the assessors detected strangeodour and avour characteristics, totally atypical in thistype of product. This may be due to elaborationproblems in the cheese batch. Hedonic measurementwas carried out at the assessors homes at the same timeas FCP and, due to the above-mentioned anomalies, thecheese was also deleted from the analysis. However,during DSM no trouble was found. This fact could bedue to intrabatch variability.Differences appeared among all cheese samples. A

sensory difference can be pointed out within the groupof Idiazabal cheeses (samples 1, 2, 4 and 5). Ordo *nezet al. (1998) detected great heterogeneity among severalIdiazabal cheeses independent of their ripening time orsmoked character. In this study, and concerning odourattributes, ripening time seemed to play a major role inodour differentiation of the products.Interpretation of the sample map (Table 2) was

obtained from the analysis of descriptors used by eachassessor. The rst dimension was characterized by

Mean2019181716151413121110987654321

2.0

1.5

1.0

.5

0.0

DIM2

DIM1

assessors

DIM1: dimension1; DIM2: dimension 2

loss

Fig. 1. Individual assessors loss for the bidimensional OVERALS

odour analysis.

2.00.0-2.0

2.0

0.0

-2.0

.7

.

..

.

.

.

.4

86

5

1

2

Dimension 1

Dim

ensio

n 2

Fig. 2. Bidimensional cheese sample plot for OVERALS odour

analysis. Details on cheese samples can be found in Table 1.


sensory attributes considered as strong or very intensewith positive coefcients (odour intensity, animal)opposite the lactic notes in the negative zone (cream,yoghurt). The second dimension was dened withpositive values by milky, sweet and owery sensorynotes, while odour intensity and animallic notescharacterize the negative axis. Intensity and often-associated terms seemed to increase diagonally, fromtop left to bottom right.Considering the above axis denition, it can be

pointed out that ripened Idiazabal cheeses (samples 4and 5) were the most intense in odour, characterized byanimal notes, especially smoked variety (Fig. 2). Orti-gosa et al. (1999) in studying raw ewes milk cheesesobserved a very big increase in terms such as odourintensity during ripening. Muir and Hunter (1992)pointed out the same effect studying the ripening ofCheddar cheese, whereas creamy notes decreased. Onthe other hand, young Idiazabal samples (samples 1 and2) were perceived as milky and sweet cheeses. Roncal(sample 6), Castellano (sample 8) and Manchego(sample 7) cheeses were described as intense in odourbut with several milky notes (cream, candy, sweet). It isinteresting to note that although Manchego cheese hadthe same ripening period as Idiazabal matured variety,its sensory characteristics were different. This could bedue to the different composition and elaborationprocedures of both cheeses and to the use of milkpasteurization during Manchego manufacturing.Some sensory attributes were clearly identied as

bidimensional, that is to say that they are not beingemployed in the same way by all the assessors (van derBurg & Dijksterhuis, 1996). This effect has been pointedout in similar studies. Jack et al. (1993) observed that thesame term was used differently by several panelists to

describe the colour of Cheddar cheeses. Attributebidimensionality makes sample maps difcult to inter-pret, being one of the major disadvantages of FCPtechniques, in which assessors consensus does not exist.

3.3. Flavour

The bidimensional OVERALS solution for avourterms had eigenvalues of 0.981 and 0.875 for rst andsecond dimension, respectively, giving an overall tnessof 1.856. This value is higher than for odour analysistness (1.472). Williams and Langron (1984) in a studyon commercial ports also observed a higher consensusamong assessors for avour terms than for odour. Thismay be due, as suggested by Lesschaeve and Issanchou(1997) to the fact that panelists are more accustomed todescribing mouthfeel sensations than odour stimulus.Fig. 3 shows the existence of 7 assessors with null loss

values. Mean loss values were 0.144 and 0.501 for

Table 2

Denition of bidimensional OVERALS solution for odour analysis

Dimension 1a Dimension 2

+ +

Intensity1 (0.765) Cream15 (0.735) Flowers4 (0.808) Concentrated milk1(0.504)

Butyric1 (0.641) Fresh17 (0.757) Yoghurt5 (0.505) Acid1 (0.788)

Vegetable2 (0.500) Lactic18 (0.850) Fresh5 (0.736) Intensity4 (0.691)

Intensity5 (0.753) Yoghurt20 (0.596) Cream7 (0.878) Animal7 (0.691)

Vegetable8 (0.637) Fruity9 (0.708) Sweat7 (0.568)

Ripened11 (0.939) Candy9 (0.500) Intensity7 (0.568)

Intensity11 (0.726) Milk15 (0.500) Acid9 (0.599)

Intensity12 (0.753) Sweet18 (0.739) Animal9 (0.576)

Intensity13 (0.753) Sweet Cheese20 (0.660) Intensity10 (0.632)

Sweat13 (0.644) Rancid11 (0.734)

Animal14 (0.918) Smoke12 (0.788)

Intensity16 (0.570) Intensity15 (0.691)

Intensity19 (0.596) Acid17 (0.696)

Moisture19 (0.753) Barn18 (0.788)

Intensity20 (0.637)

aNumbers after each descriptor identify sensory assessor who employed it during free choice proling.

DIM2

DIM1

2019181716151413121110987654321

1.0

.8

.6

.4

.2

0.0

loss

assessors

Mean



avour analysis.


avour and odour, respectively, indicating better con-sensus among panelists when evaluating avour proper-ties. In this case, panelist number 4 showed the highestloss (0.815), while number 6 got relatively low lossvalues (0.209). This fact corroborates the hypothesisthat some individuals have greater ability to describe aspecic type of stimulus (Lesschaeve & Issanchou,1997).The bidimensional sample plot (Fig. 4) for avour

shows clear differences when compared with odoursample plot (Fig. 2). There was a cluster formed byIdiazabal cheese samples (samples 2, 4 and 5, except forfarmhouse origin, sample 1), Roncal (sample 6) andCastellano (sample 8). Manchego (sample 7) and farm-house Idiazabal (sample 1) cheeses were located totallyindependent from the rest of the varieties. Samples werespread especially along dimension 1, except for Man-chego (sample 7) that is found at the negative end ofdimension 2.Idiazabal cheese samples 2, 4 and 5 were located very

near to the origin (0.0) of the sample plot (Fig. 4). Assuggested by Lawless, Sheng, and Knoops (1995), thistendency should be interpreted as a lack of cleardifferences among samples involved in this clustering.Ripened Idiazabal cheeses (samples 4 and 5) were closetogether. Although (sample 5) was smoked, the assessors

did not perceive a great difference between the twosamples. P!erez Elortondo, Albisu and Barcina (1999a)explained that the smoking treatment within IdiazabalDesignation of Origin is a very light process frequentlyonly perceived on the rind, and only very slightly in themass.In order to aid interpretation of this sample distribu-

tion, in the same way as for odour analysis, sensoryattributes weights were studied (Table 3). The number ofattributes with high correlation coefcients deningeach dimension was lower than in odour analysis.Dimension 1 was dened in the positive zone by termsthat could be considered strong or intense (bitter,pungent, intensity, sharp) while the negative zone wascomposed of a number of terms which could bedescribed as sweet, fresh, acid and pungent. Dimension2 was dened in the positive axis by persistence,intensity and salty, whereas negatively correlated termswere astringency, old, dry (typical attributes for maturedsamples) and toasty, fresh (typical terms for pasteurizedvarieties). In a study on a variety of traditional Spanishewes milk cheeses, it was found that toasted sensoryattributes are especially important in pasteurized cheeses(Ortigosa, Torre, & Izco, 2000). In the present study, thediversity of attributes with different meanings deningeach dimension makes it difcult to interpret samplelocations.As a summary, within avour FCP, sensory differ-

ences were perceived among samples, but the attributesdening different dimensions did not clearly describethese differences.

3.4. Texture

The bidimensional OVERALS solution for textureterms had eigenvalues of 0.955 and 0.872 for rst andsecond dimension, respectively, giving a total tnessequal to 1.827. This value was very similar to thatobtained for avour analysis, and in any case aboveodour total tness. Mean loss was 0.128, below odourand avour values, showing a higher consensus among

.7

.4.

8.

6.

5

.

1 .2

2.50.0-2.5

2.5

0.0

-2.5

.

Dimension 1

Dim

ensio

n 2

Fig. 4. Bidimensional cheese sample plot for OVERALS avour


Table 3

Denition of bidimensional OVERALS solution for avour analysis


+ +

Intensity2 (0.797) Acid1 (0.905) Milk2 (0.507) Fresh5 (0.727)

Salty2 (0.564) Pungent3 (0.905) Intensity3 (0.665) Toasty9 (0.961)

Intensity6 (0.580) Bitter9 (0.583) Salty7 (0.586) Astringent14 (0.727)

Aftertaste8 (0,525) Fresh11 (0.545) Persistence19 (0.698) Old15 (0.685)

Intensity9 (0.628) Sweet13 (0.701) Animal16 (0.587)

Whey9 (0.723) Acid14 (0.905) Dry18 (0.961)

Pungent16 (0.807) Pungent15 (0.730)

Bitter18 (0.736) Fresh17 (0.575)

Sharp19 (0.644) Animal17 (0.512)



panelists. As observed in Fig. 5, there were 4 assessorswith null loss, numbers 2, 4, 9 and 10. Panelist number17 showed the highest loss of all (1.002).As for odour and avour analysis, the texture sample

plot showed differences among samples and withprevious analysis (Fig. 6). Williams and Langron(1984) reported different samples maps for odour,avour and texture attributes using FCP to describeports.Cheese groups could be identied along dimension 2

(Fig. 6), with Roncal (sample 6) and ripened Idiazabal(samples 4 and 5) cheeses on one side, while farmhouseIdiazabal (sample 1) and Castellano (sample 8) formedanother group. Young Idiazabal (sample 2) andespecially the Manchego (sample 7) cheeses appearedwell differentiated from the rest of the samples.Manchego cheese seemed to be rmer than the othersamples. All samples except for Manchego cheese(sample 7) showed almost constant value for dimension1, spread along dimension 2. It was interesting to notethe extreme values for dimension 1 between youngIdiazabal cheeses, farmhouse (sample 1) and industrialcheeses (sample 2). This dimension was characterized bythe attribute hardness in the negative zone oppositefriability with positive scores. Along this line, Muir,

Banks, and Hunter (1997) identied more sensoryrelevance differences for farmhouse Cheddar comparedwith industrial cheeses.Texture attribute weights are shown in Table 4.

Dimension 1 was characterized positively by creaminess,adhesiveness and elasticity, and negatively by hardness,graininess and greasyness. Dimension 2 showed creami-ness and elasticity with positive coefcients, whereashardness and wrinkliness dened the negative axis.Hardness (juxtaposed to creaminess) was negativelyrelated to both dimensions 1 and 2. Several termspresent bidimensional character, dimension 1 and 2being very similar.

3.5. Direct similarity measurements (DSM)

From the group of 20 assessors initially participatingin this study only 18 performed the direct similarityexercise, 2 leaving the group.Bidimensional INDSCAL solution showed 0.32 and

0.31 scores for stress and RSQ, respectively. Thesevalues reveal the existence of individual differencesamong assessors at the time of sample evaluation(Kruskal, 1964). These high stress values are quitecommon when using INDSCAL analysis even withhighly trained assessors (Bertino & Lawless, 1993).For this reason, INDSCAL analysis should be

employed to take all data variability into account.Fig. 7 shows the existence of differences among asses-sors sensations, perceptions or cognitions for cheesesamples. Panelists appeared clearly dispersed mainlyalong dimension 2, with values from 0.2 to 0.6approximately, while values in dimension 1 were moreclustered (0.20.5), except for assessor 15. This maysuggest that assessor 15 clustered the samples using adifferent pattern to the rest of the group.Fig. 8 shows the sample spatial map derived from

INDSCAL. It can be clearly appreciated that there werethree differentiated groups of samples: ripened Idiazabal(sample 4 and 5), young Idiazabal (samples 1, 2 and 3)and the rest of the cheese varieties (Roncal, Manchego,Castellano, samples 6, 7 and 8, respectively) having alower clustering level. Assessors perceived similarities inIdiazabal cheese sample with regard to their ripeningtime, whether smoked or not. This fact was alsoobserved for FCP results.In this case, cheese clustering was more evident than

for odour, avour or texture OVERALS solutions. It isclear that untrained assessor result interpretation can bedone more easily using DSM, as cheese samplecharacteristics are considered as a whole.However, quantitative as well as qualitative differ-

ences can be identied and characterized in detail usingonly descriptive sensory proling techniques. This facthas also been described by several authors (Matuszewska,

.7 .4

.

8

.6

.

5

.

1

.

2

-2.5

2.0

0.0

0.0 2.5-2.0

Dimension 1

Dim

ensio

n 2

Fig. 6. Bidimensional cheese sample plot for OVERALS texture


DIM2

DIM1

2019181716151413121110987654321

1.2

1.0

.8

.6

.4

.2

0.0

loss

assessorsMean



texture analysis.


Barylko-Pikielna, Tarkkonen, Hellemann, & Tuorila,1991/2).In the present study, FCP results were not a great help

when characterizing and identifying cheese varieties indetail. Intensity attributes seemed to play an importantrole in the FCP odour map (and slightly less in theavour map), but even this aspect was more clearlyperceived in the DSM map. In this case, basic sampleinformation (especially ripening time) enables the readerto interpret the spatial sample DSM conguration. Fora detailed sensory description, the use of a trained panelwould have been of great assistance.

3.6. Hedonic measurements

Usually, a great number of assessors are required ifthe objective of the researcher is to interpret hedonicdata as representative of the global population. How-ever, the opinion of a few individuals was taken intoaccount in this study, as one of the objectives was tocompare spatial congurations using different sensorymethodologies, more than to explore overall consumerpreference structures.Two-way ANOVA (sample assessor) on hedonic

scores showed the existence of clear differences amongindividual likings po0:001: However, cheese samplesgave very similar hedonic scores ranging from 3.4 to 4.6,for young non-smoked Idiazabal (sample 2) andManchego (sample 7) cheeses, respectively (Table 5).Taking into consideration Taguchi SNR, it can be statedthat scores for Manchego cheese (sample 7) are the mostrobust as well as the highest. Pastor et al. (1996) alsoemployed this statistical index as an alternative way toassess preference data robustness.

Table 4

Denition of bidimensional OVERALS solution for texture analysis


+ +

Elasticity4 (0.591) Dry1 (0.834) Deformability1 (0.750) Hardness2 (0.774)

Pastiness6 (0.571) Graininess1 (0.779) Friability2 (0.676) Deformability2 (0.548)

Creaminess9 (0.560) Hardness7 (0.817) Firmness4 (0.516) Hardness3 (0.750)

Elasticity9 (0.533) Graininess7 (0.737) Elasticity5 (0.705) Hardness5 (0.753)

Creaminess14 (0.955) Wrinkliness10 (0.533) Friability10 (0.509) Pastiness6 (0.563)

Adhesiveness15 (0.511) Hardness12 (0.591) Wrinkliness11 (0.571) Wrinkliness8 (0.516)

Deformability16 (0.746) Greasy12 (0.986) Creaminess12 (0.628) Greasy8 (0.682)

Adhesiveness18 (0.985) Greasy13 (0.985) Adhesiveness15 (0.703) Hardness11 (0.572)

Creaminess19 (0.820) Graininess13 (0.511) Pastiness16 (0.783) Crunchy12 (0.628)

Plastic14 (0.994) Creaminess20 (0.789) Graininess18 (0.687)

Hardness15 (0.570) Wrinkliness20 (0.605)

Hardness16 (0.841)

Hardness17 (0.985)

Greasy17 (0.902)

Greasy20 (0.718)

Graininess20 (0.985)


.7

.4

.

8

.6

.

5

.

1.

2

2.00.0-2.0

2.0

0.0

-2.0

.

Dimension 1

Dim

ensio

n 2

.

3

Fig. 8. INDSCAL analysis. Multidimensional arrangement of cheese

samples based on individual direct similarity data matrices. Details on

cheese samples can be found in Table 1.

18

1716

1514

1312

11

10

9

8

7

6

5

4

3

21

.7.6.5.4.3.2

.6

.5

.4

.3

.2

Dim

ensio

n 2

Dimension 1

Fig. 7. INDSCAL analysis. Dimensional weights for assessors evalu-

ating ewes milk cheeses using direct similarity measures.


As suggested by Yackinous, Wee, and Guinard(1999), if assessor preference criteria are not homo-geneous, conclusions reached using mean preferencescores may be worthless. When dealing with these cases,it is better to study panelists individual likings. Internalpreference mapping (MDPREF) showed clear differ-ences among consumers likings (Fig. 9). The two rstbiplot dimensions explained 34.5% and 24.3% ofvariance, respectively. A group of 10 consumerspreferred the Roncal (sample 6), Manchego (sample 7)and Castellano (sample 8), the rest being distributedaround the whole preference ellipse.Sample conguration in Fig. 9 showed similarities

with odour OVERALS and INDSCAL solution, withManchego (sample 7), Roncal (sample 6) and Castellano(sample 8) cheeses quite near one another. This factcould be indicating that odour characteristics play animportant role at the time of dening consumerpreference for these types of samples.

3.7. Sensory methodology correlation

Correlation matrix for the rst two dimensions of thedifferent sensory methodologies employed in this studyis shown in Table 6. It can be appreciated that the rstINDSCAL dimension showed a positive signicantcorrelation po0:05 with ODOR1, FLAV1 and nega-

tive with MDPREF1. These high correlation coefcientscould be due to the fact that odour and avour intensity(cheese maturity) was a common factor within thesedimensions (ODOUR1, FLAV1 and INDS1). However,it is not so with texture. As pointed out by Gilbert andHeymann (1995) in a similar study, it may be due to thedifferent nature of characteristics taken into considera-tion when using each sensory methodology.Differentiation of the Idiazabal cheese from the others

could be the common factor between INDS1 andMDPREF1. Moreover, Falahee and MacRae (1995)underlined that when performing DSM, hedonic dimen-sion may be playing a very decisive role in sampleclassication. This may be the explanation for the highercorrelation coefcient between INDS1 and MDPREF1.TEXT1 and FLAV2 present a signicant corre-lation coefcient po0:01; mainly due to sample 7(Manchego).

4. Conclusions

The FCP has proved to be successful in the sensorydescription of several food products. However, in thisstudy the maps obtained by means of this techniquewere not easy to interpret without any other externalsource of information (e.g. traditional sensory prole,familiarity with cheeses, etc.). Manchego cheese varietyshowed the highest sensory differences among samples,these differences being the most easily interpretableones. Cheese samples showed specic sensory character-istics mainly based on ripening time, although thisevidence may be considered tenuous mainly due to thelack of panel consensus during attribute scoring. Greatindividual variability was observed for the consideredtechniques. These problems could have been avoided bythe use of a trained panel.The main dimensions showed signicant correlation

for almost every multidimensional solution. Theseresults described the similarities between the underlyingdimensions once assessors are evaluating the samples.Therefore, it can be concluded that the sensory test to beemployed should be carefully selected and, if possible,validated with allied methods depending on the

Table 5

Two-way ANOVA (sample assessor) and Taguchi signal-to-noise ratio (SNR) for hedonic measures

Source dF SS F Sign.

Samples 6 19.486 3.248 0.153

Assessors 19 113.029 5.949 0.000

Total error 139 363.886

Samplesa 7 5 8 6 4 1 2

Hedonic score 4.6 4.4 4.1 4.0 3.9 3.6 3.4

Taguchi SNR 12.35 11.56 10.49 10.12 10.51 9.01 8.94

a Information on cheese codes can be found in Table 1.

1.50.0-1.5

1.5

0.0

-1.5

Dimension 1

Dim

ensio

n 2 2

514

67

8

Fig. 9. Bidimensional MDPREF biplot showing cheese samples

(triangles) and consumers (dots). Details on cheese samples can be

found in Table 1.


objectives of the study as well as the availability of atrained panel or time requirements.

Acknowledgements

This work was nanced by Universidad del Pa!sVasco (UPV 101.123-TA095/96). Pedro B!arcenasthanks the Departamento de Industria, Agricultura yPesca of the Basque Government for a fellowship.Members of the ewes milk cheese panel are thanked fortheir enthusiastic participation in this study.

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1 2 3 4 5 6 7 8 9 10

1 INDS1a 1.000

2 INDS2 0.044 1.0003 ODOR1 0.747b 0.509 1.000

4 ODOR2 0.552 0.509 0.001 1.0005 FLAV1 0.791b 0.235 0.635 0.382 1.000

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Comparison of free choice profiling, direct similarity measurements and hedonic data for ewes milk cheeses sensory evaluationIntroductionMaterials and methodsCheese samplesAssessorsSensory analysisStatistical analyses

Results and discussionFree choice profiling (FCP)OdourFlavourTextureDirect similarity measurements (DSM)Hedonic measurementsSensory methodology correlation

ConclusionsAcknowledgementsReferences

cheeses sensory evaluation

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