Could selection tests detect the future performance of descriptive panellists?

Food @alrQ and Prefnmcc Vol. 7, No. 3/4, pp. 177-183, 1996 Copyright 0 1996 Elsevier Science Ltd Printed in Great Britain. All tights reserved ELSEVIER

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COULDSELECTIONTESTS DETECTTHEFUTURE PERFORMANCEOFDESCRIPTIVEPANELLISTS? I. Lesschaeve*

&S. lssanchoui

INRA Laboratoire de Recherches sur les ArBmes, B.V. 1540, 21034, Dijon Cedex, France (Accepted 2 7 April 1996)

sensory literature, the time dedicated to the training of descriptive panels varies from 10 hours (Stone & Sidel, 1985) to more than 100 hours (Meilgaard et al., 1991;

ABSTRACT

Heisserer & Chambers IV, 1993). To reduce this time, some authors have suggested the use of a generic pre-specified vocabulary for a particular type of product (e.g. beer aroma wheel, Meilgaard et al., 1982; wine aroma wheel, Noble et al., 1984). In that case, some characteristics may be missed if the products do not fit the sensory space ana-

This paper discusses the appropriateness of screening tests in explaining descriptive panellist performances. It is based on a case study aimed at forming a descriptive panel capable of jlavour projling Camembert cheeses. Eighteen subjects were selected using four sensory tasks evaluating smell sensitivities, olfactory knowledge, odour memory and descriptive ability. Three additional tests were proposed during the 45 hour training to evaluate the recognition memoryfor odours, the concentration and the verbal creativity abilities. Panellist performances were determined on repeatability and discrimination abilities, and on the complexi of the individual sensory space. Some signiJicant relationships were observed. Indeed, to be discriminant on flavour attributes, subjects must either have an initial olfactive culture or have a good ability to memorize olfactive stimuli. Copyright 0 I996 Elsevier Science Ltd

lysed for the vocabulary development. Aiming to discard the training altogether, Williams & Langron (1984) proposed a new technique, the Free Choice Profiling (FCP) which enables the profiling of a set of food products with naive subjects using their own vocabulary to describe the sensory characteristics. No vocabulary alignment is thus required. Although sophisticated statistical treatments (Generalized Procrustes Analysis (Gower, 1975) or STATIS (Schlich, 1996)) permit the analysis of such data, the interpretation of the sensory map with the individual attributes is often confusing. In many cases, only the clear differences in taste or texture rather than odour attributes can be unambiguously described. This is perhaps because, in their everyday life, subjects have learnt to associate their gustatory and tactile perceptions with a specific vocabulary (e.g. saltiness and sweetness or hardness), whereas the naming of odours is often more difficult due to the heterogeneity of the vocabulary. The verbalization of odour perception is strongly dependent on the subject’s experience with the odorants (Engen, 1987), and no fixed standards are defined in everyday life.

INTRODUCTION The quantitative descriptive profile of food products or conventional profiling, as defined by Lyon et al. ( 1992), could actually be considered as one of the most complex sensory tasks (Stone & Sidel, 1985). Indeed, panellists have to analyse their perceptions when tasting the product, verbalize them and quantify them relative to the product

Without doubt FCP is a very useful technique to quickly obtain a sensory map with the main differences between products. However, when more precise results are expected, a conventional profiling method remains the more appropriate technique. As mentioned by Ein-

sensory space under study. Besides its complexity, this sensory methodology is known to provide precise and consistent results, providing that the panel has been selected, properly trained and maintained (Einstein, 1991). These two conditions require the investment of a lot of resources, particularly during the training phase which consists primarily of vocabulary development and alignment (Einstein, 1991; Lyon et al., 1992). According to the

stein ( 1991)) the training of descriptive panels should not be too limited because it is likely to lead to an incomplete description of the sensory characteristics of the products. Moreover, Wolters & Allchurch (1994) observed that the longer the training period, the better the product discrimination is and the stronger the agreement among panellists. Chambers & Smith (1993) have showed the superiority of product-specific training over the general sensory experience in improving panel performances.

*Author to whom correspondence should be addressed. E-mail: [email protected] tE-mail: [email protected] 177

I. Lesschaeve,

178

S. Issanchou

As the time dedicated ble

without

propose

the

to examine

procedures

to the training

affecting

the

to select

is hardly reduci-

measurement

quality,

appropriateness

the right

persons

of screening to perform

Two

the

profiling task. If the selection tests could detect the future performance

of descriptive

panellists,

train a panel with high abilities available

panellists thou,

in

the

(ASTM,

1992; ISO,

selection

and

Meilgaard

1993). However,

profiling

texture

described

profiling

et al.,

tests and on the profiling

(in 6 minutes)

on these selection and rice

principle: Profiling

if relationships

test (Swets

case study which had as its main purpose the study of the impact of micro-organism associations on the sensory cheese (Molimard,

the abilities

related

description,

because,

1994). The sen-

of the flavour was the main objective

of this study. The selection

to examine

ability

dots. Subjects

have to

index is equal to

of 4 dot groups found on the whole

learned

attributes.

flavour

Panellists

the sensation this

development

to associate

and alignment, perceptions

have

and the associated

information

when

This task is particularly

lary is available order to evaluate subjects,

analysing

a

product

difficult when working

a test was proposed by Lesschaeve

(1995),

out of 103 candidates

by Rabin

& Cain (1984).

Memory

test is also conceived

memory

of the subjects,

were recruited

after passing

by Issanchou

Their olfactory abilities were evaluated

the

et al. (1995).

with four tests out

the seven described in that paper: Familiar

Odour Recog-

and was in fact an adaptation

descriptive

(where only olfactory

memory

of each test are summarized

were scored). in Table

1.

vocabu-

Although

& Issanchou

of the test designed the Grouping

to evaluate

an alternative

Odour

the olfactory

test was investi-

gated with a process which is very close to that which a

nition, Grouping Odour Memory, Ranking and Description oral attributes

to

label and to

contrary to gustatory sensations. In the Olfactory Recognition Memory of

Eighteen subjects ( 1 man, 17 women; mean age = 33 y-0.) described

with

to be able

on olfactory sensations for which no standardized

procedure

The

of 50 lines; each line is made

the vocabulary

panellists

sample.

Screening procedures

parameters

is used to

questionnaire.

retrieve

selection

NL)

to

T.I.B.

of the panellists.

one line. The concentration number

memorize

METHODS

a panellist

The Bourdon

bv, CALISSE,

is composed

appropriate

complex sensory modality and the most difficult to describe.

creativity

mark all the groups of 4 dots. Eight seconds are allowed

During

flavour is the most

in 6 min-

The

requires

intensively.

the concentration

the mean

tests were mainly focused on

3 letters in

but using the same item.

of 25 groups of 3, 4 or 5 black

to the sense of smell and to flavour from experience,

per generated

& Zeitlinger

questionnaire

from a

terms of four

of three sets of pictures

sensory characteristics

filing performances.

data were obtained

1973,

This test is com-

and search of words containing

1 point

estimate

properties of Camembert

decorations

be able to concentrate

The aim of this paper is to determine

& Hemenway, 1994).

of a

index is the sum of the 3 scores.

could be observed between selection tests and flavour pro-

sory characterization

(Hoepfner

This

adaptation

a given order (6 sets of 3 letters are examined

a complex

profile through simple selection tests.

Sensory

test

a French

& Allchurch,

fac-

and alignment.

utes). Each part is scored separately

that it was not

in completing

development

words (in 4 minutes),

to select a

Sauce

panellists.

posed of three parts: search of synonymous

panellists

Rousset-Akrim

of Bolognese

creativity

were eval-

their potential

of panellists is an important

by performing

cited by Wolters

abilities

to estimate

good descriptive

The verbal creativity

verbal

Issan-

between

of the

They concluded

the ability

1991;

procedure

Performances

samples were examined.

impact in detecting

was evaluated

descriptive

relationships

Recently,

a screening

panel.

possible to predict

screen

performances

have not been widely studied. et al. (1995)

to

and one sensory

tor for the vocabulary

Many recommendations

literature

1981;

conven-

non sensory

uated at the end of training

we would expect to

for performing

tional profiling more efficiently. are

Evaluation of complementary abilities

we

The

(Banks,

panellist

is asked. The

is estimated

by

the

olfactory

detectability

recognition index,

d’

1970).

TABLE 1. Summary description of the selection tests Selection test

Ability evaluated

Familiar Odour Recognition Odour Memory Grouping

Olfactory Olfactory

Ranking

Odour intensity discrimination Verbal ability to express sensory perceptions

Description

of food flavour

knowledge memory

10 odorant 15 odorant

Stimuli

Sensory task

solutions solutions

Match an odorant solution with a label Make 5 groups of three odorants, based on persona1 criteria, consistently one week apart Rank the flasks according to the mushroom odour intensity Describe as precisely as possible the olfactory oral sensations

5 solutions of I-octen-3-01 ‘2 Camembert

cheese samples

The Future Performance of Descriptive Panellists

TABLE 2. Olfactory oral sensations used to profile the experimental Camembert cheeses Global Intensity Milk Cream Butter Cancoillotte Melted cheese l

Fermented Cheese

Blue Cheese

We chose both univariate and multivariate statistical indices to estimate the performance of the panel.

Univariate indices

Musty Cardboard Nutty * Plastic

Cowshed Rancid Cabbage Ammoniac Mushroom Dead leaves

179

Smoky

Not used by the panel to discriminate significantly @ < 0.05) between the 16 samples.

l

Experimental design for the sensory measurements

The discrimination power is estimated by DISCanova, which is the number of discriminant attributes used by one panellist. This index is obtained by performing an ANOVA by subject (p < 0.10). The repeatability was estimated by the index Drift-Mood proposed by Schlich (1994) in the procedure GRAPES. Which describes a session effect. The pure error was estimated by the index Unreliability, also proposed in GRAPES.

Multivariate

Training A training

of forty-five hours was planned to develop the descriptive vocabulary and to align it among panellists, as well as to train in scoring on the linear scale.

Sensory measurements The panel evaluated sixteen experimental Camembert cheeses with 37 descriptors describing texture (11 items), taste (3), mouthfeel (1)) olfactory oral sensations (19) and aftertaste (3). For the purpose of this paper, only the nineteen olfactory oral descriptors will be considered (see Table 2). Four cheeses were evaluated per week, twice a week, which enables the control of a session effect. Four weeks were necessary to examine the sixteen experiexperimental information mental cheeses. Additional may be found in Molimard (1994).

Definition of the panellist performance indices A number of papers have been published recently describing simple tools to control panellist performances (e.g. Naes, 1990; Naes et al., 1994; Pritchett-Mangan, 1992; Schlich, 1994). Although the techniques differed (graphical, univariate or multivariate statistical analysis), there is a consensus on the parameters of panellist performance for quantitative descriptive profiling. These are reliability, consistency, discrimination power and complexity of perception of the sensory space.

indices

Three indices were defined after performing a stepwise discriminant analysis by subject (p < 0.10). DISCstep is the number of non-related discriminant attributes given by the variable selection. The discrimination is evaluated with DISCascc which is the average squared canonical correlation; it indicates the power of a panellist to discriminate the 16 samples in the global sensory space. When DISCascc is null, it means that the subject did not perceive any difference between the samples. When it is close to one, it means that the subject was able to discriminate each sample from the others. The complexity of the perception of the sensory space is estimated by the number of discriminant axes used by the panellist (DISCaxis; p < 0.10).

Statistical analysis To study the relationships between selection and profiling performances, the selected panellists are categorized in two classes for each selection task, according to their respective performance. The score distribution of each test is examined to define the criterion which will split the panel into 2 balanced classes, as far as possible. Class 1 will categorize the good performances and Class 2 the weak ones. This criterion is defined as the median when the distribution was roughly normal and as the breaking point when the normality was rejected. The criteria are reported in Table 3. The basic hypothesis of this work is

TABLE 3. Categorization criteria and performances of the selected panel for each selection and additional tests Selection Test

Class 1 (good scores)

Familiar Odour Recognition Odour Grouping Memory Description test Ranking Recognition Memory Odours (d’) Concentration Verbal creativity

for

= 10 (n= 10) >5 >5 = 10 > 1

(n=9) (n=8) (n= 13) (n=8)

> 5.8 (n=8) > 108.5 (n=9)

Class 2 (weak scores) < 10 <5 55 < 10
(n=8) (n=9) (n= 10) (n=5) (n=lO)

5 5.8 (n= 10) 5 108.5 (n=9)

Possible score Mean score range

Coefficient of Variation (%)

Observed score range

O-10 O-10 0-00 Sl O-7.5

8.7

16.8

5.8 7.2 0.97 1.18

35.8 73.7 4.7 74.9

2-10 O-18 0.9-l -0.1 T-4.03

O-8 o-00

5.93 108.8

9.41 24.9

5.04-6.96 73-164

6-10

180

I. Lesschaeve, S. Issanchou

that high selection scores involve high quantitative descriptive profiling performances. To study the validity of this assumption, a Student l-test was performed between selection test classes for each profiling performance index. When a profiling performance index does not fit the normal distribution, it is also categorized in two classes; a chi-squared test is then performed on the cross-tabulation of selection and profiling performance index classes. All the statistical analysis are conducted using S.A.S. (1988).

RESULTS

AND

DISCUSSION

Selection and additional test performances The additional tests were performed to evaluate supplementary abilities which were not expressed by the other selection tasks. To verify the independence of these two kinds of ability measurement, Spearman correlation coefficients were calculated between additional tests and selection tests. Only three correlations were found to be significant (j.~ ~0.10): Grouping Odour Memory and Concentration tests (r = 0.43, p = 0.07), Ranking and Concentration tests (r = 0.41, p = 0.09), Description and Verbal creativity tests (r = 0.63, p = 0.005). The latter result was expected as we assumed that panellists who had a high verbal creativity score would easily find more words to express their perceptions when tasting food products. The description task is easy to design and to score compared to the Verbal Creativity test; it should be preferred in the future screening tests for descriptive panel. The partial links between grouping odour memory or ranking and concentration abilities could be interpreted by the common need for subjects to concentrate on the task in order to succeed. These correlation coefficients are quite low which suggests only partial TABLE 4. Pearson rank correlation coefficients DlSCanova(a)

DISCstep

1 0.60 2 0.71 ’ 0.87 ’

DISCanova DISCstep DI SCascc DISCaxis Drift-Mood Unreliability

-0%

on profiling

To evaluate the appropriateness of each index to describe the quantitative descriptive profiling performance, Pearson correlation coefficients were calculated to estimate the links between these variables and are reported in Table 4. All the discrimination power indices are more or less dependent on each other as they are estimated on the same basis: the use of attributes to describe the sensory space. DISCanova and DISCstep are only partly related since the coefficient is equal to 0.60. This suggests the necessity to take into account the redundancy of attributes to study panellist performances. Indeed, the number of discriminant attributes obtained by the ANOVA can be artificially high if most of the attributes are correlated. This does not occur with the stepwise variable selection, as already mentioned by Rousset-Akrim et al. (1995). However, in the present paper, a high DISCanova led to the use of a large number of discriminant axes to describe the global sensory space. This result indices

DISCascc(c)

DISCaxis’

1 0.73 ’ -0.74 ’ -0.54 s

I ns

I

ns

0.78

0.61 2 -0.66 2 -0.47 3

I: p < 0.001; 2: p < 0.01; 3: p < 0.05; 4: DISCaxis

Profiling performances

performance

1 0.96 ’

3

relationships between these indices. They do not measure exactly the same abilities. Surprisingly, the two tasks evaluating olfactory memory (i.e. Grouping Odour Memory and Recognition Memory for Odours) do not seem to measure the same ability. The mean performances of the selected panel are described in Table 3. The coefficient of variation (CV) obtained for the Familiar Odour Recognition and Ranking test is quite low ( < 20%) which suggests a certain homogeneity of abilities within the panel. The same observation could be made for Concentration ability scores (CV < 10%). However, for the two other selection tests as well as for Recognition Memory for Odours and Verbal creativity, this variation is large (CV > 20%), which reflects the difficulty of forming a perfectly homogeneous panel with the best performance at each task, as was already underlined by Issanchou et al. (1995).

is a non normal

variable.

Spearman

STEPDISC

rank correlation

it and the others. a: number

of individual

discriminant

b: number

of individual

non related and discriminant attributes (PROC

c: Average

squared

d: number e: session

canonical

of discriminant effect index

F: pure error index

attributes

correlation

axis (PROC

(ANOVA)

(PROC CANDISC

STEPDISC in S.A.S.)

in S.A.S.)

Drift-Mood(e)

in S.A.S.)

Unreliability(f)

’

coefficient

1 is computed

between

The Future Performance of Descriptive Panellists

in the way of analysing the 16 cheese samples. However, the mean values of these performances permit us to describe aromatic differences between cheeses with about five discriminant and non-related attributes (out of 19) and the complexity of the sensory space perceived by the panel is more than 2 axes.

could be interesting information to the training process. Moreover, the two repeatability indices are also linked which means that, in this set of data, subjects who had a high pure error index also tend to have a high session deviation effect. Although relations between most of the performance indices are observed, only DISCstep and DISCascc could be considered as redundant variables (r = 0.96). This relationship is particularly interesting: it means that each selected attribute contributes to increase, with sharpness, the discrimination power of the panellists in the global sensory space. This is confirmed by the links existing between DISCascc or DISCstep and DISCaxis (the number of discriminant axes). The mean scores of each index are reported in Table 5. Considering the mean scores of Unreliability and DriftMood indices, we observed that they are lower than 1, which reflects good performances. The panel can therefore be considered as a precise tool. However, the oral sensations of the 16 description of olfactory Camembert cheeses with the 19 items remains slightly different among the panellists; indeed, about 7 discriminant attributes are used by subjects, but the CV is quite large (48%). The same remark can be made for DISCstep, DISCascc or DISCaxis. Although the panel was intensively trained, a certain heterogeneity remains TABLE

5.

Relationships between selection and profiling performances Results are reported in Table 6. The prior abilities of the subjects measured with the Ranking test, the Description test, the Verbal Creativity test and the Grouping Odour Memory test do not influence their profiling performances. The score variability of the Ranking test may have been too narrow to point out any effect. The descriptive ability, strongly related to the verbal creativity, is in fact mainly important at the beginning of the training when subjects have to develop an appropriate descriptive vocabulary, and appears minor during profiling measurements. So, the absence of this latter effect on profiling performances is not surprising. However, in spite of the fact that a low coefficient of variation is observed on data, the Familiar Odour Recognition test seems to have a potential predictive

Profiling performance description.

Test

Mean

Coefficient of variation %

DISCanova’

7.22

48.6

2

DISCstep’ DISCascc3 DISCaxis Drift-Mood’ Unreliability6

4.92 0.22 2.5 0.79 0.84

40.2 31.8 39.2 20.2 9.5

1 0.05 1 0.57 0.69

‘: number

of individual

STEPDISC

in S.A.S.);

(PROC

CANDISC

TABLE

6. Relationships

discriminant 3: Average

in S.A.S.);

attributes squared

(ANOVA);

canonical

(PROC

Maximum score 16 8 0.33 4 1.14 1.02

non related and discriminant

STEPDISC

in S.A.S.);

4: number

attributes

(PROC

of discriminant

axis

5: session effect index; % pure error index

between selection and profiling performances Index DISC

Familiar Odour Recognition Odour Grouping Memory Ranking Description Recognition Memory for Odours Concentration

‘: number

of individual in S.A.S.);

t= 1.94 p = 0.069 ns

ascc3

t=2.08 p = 0.053 ns

DISC

x2.= 3.60 p=o.o59 ns

ns

ns

t =Y.50 p=o.o02

t=:l6 p = 0.0007

axis4

Drift-mood5

ns

ns

ns

ns ns ns

ns ns ns

ns ns ns

ns

ns

ns

ns

ns

ns

ns

ns

ns

3: Average

attributes squared

(ANOVA);

canonical

? number

correlation

of individual

(PROC

5: session effect index; ‘? pure error index

Unreliability’

ns

ns

discriminant

in S.A.S.);

DISC

DISC step*

ns ns ns

Creativity

CANDISC

anova’

t=2.30 p = 0.035 ns

STEPDISC (PROC

Minimum score

*: number of individual

correlation

Test

Verbal

181

non related and discriminant

STEPDISC

in S.A.S.);

4: number

t= -1.87 p=o.o79 ns attributes

(PROC

of discriminant

axis

182

I. Lesschaeve, S. Issanchou

value for panellist performances. Indeed, subjects who seemed to use more attributes to discriminate significantly between the 16 samples (DISCanova, DISCstep) and those who seemed to have a higher discrimination power in the global sensory space (DISCascc) had obtained higher scores on the Familiar Odour Recognition test. Moreover, these subjects tended to describe the aromatic sensory space of the cheeses with more dimensions (DISCaxis). Testing the prior olfactory knowledge or culture seems to be essential when the oral olfactory sensations have to be described and measured by the future panellists. Indeed, when people smell unfamiliar stimuli they have no prior ‘name-odour’ association available and do not have the ability to create it quickly in laboratory conditions (Cain, 1979). Although the sensory modalities were different (texture/oral olfactory perceptions), this result is in accordance with those obtained by RoussetAkrim et al. ( 1995): they found only one significant relationship between the Texture Property Knowledge test and the discrimination power on the rice texture profile (r = 0.42, p < 0.05). These two results demonstrate the low contribution of training procedure to align performance and the preponderance of previous knowledge over the learning during the profiling training. Screening candidates on their previous knowledge of sensory properties may be a clue to reducing the vocabulary alignment period, particularly if it is verified that candidates had a common knowledge. Performances on the Grouping Odour Memory test - which evaluates the olfactory memory of the subjects - did not affect the profiling measurements, contrary to our expectations. This result is actually surprising since a strong effect is observed for the Recognition Memory for Odour test. Indeed, subjects who obtained higher scores at this task are shown to use more nonrelated attributes to discriminate between the samples (DISCstep) and to have a stronger discrimination power in the global sensory space (DISCascc). The observed effect may be explained by the design of the test which is closer to what is asked of a descriptive panellist, i.e. to perceive a sensation and to associate the correct label to it. However, this additional test was not performed at the selection phase, but at the end of training. The sensory experience acquired by the subjects during this time may have influenced their performances. Desor & Beauchamp (1974) showed that training on the testing stimuli could improve the recognition performances; but we previously observed that the sensory experience of subjects do not influence these performances (Lesschaeve & Issanchou, 1995). The Recognition Memory for Odour test seems therefore more interesting to be tested at the screening phase of the panel than the Grouping Odour Memory test. Finally this result underlined the importance of screening the future descriptive panellists on their Recognition Memory for Odours if the role of this ability on the profiling performances is confirmed by further studies.

The most discriminating panellists performed better at the Familiar Odour Recognition test or at the Recognition Memory for Odour test. However, the high performances observed on these two screening tests are not related (x * = 2.2, p = 0.13); this suggests the need to focus the selection on both directions. Finally, an interesting relationship is observed between the Unreliability index and the Concentration Ability test. Although this level of significance is only 8%, it seems that panellists who have difficulty in concentrating tend to be more unreliable than the others. If this phenomenon is confirmed on other data, it means that a non-sensory test could estimate the tendency of a candidate to give inconsistent measurements in spite of intensive training on the methodology and products under study. Wolters & Allchurch (1994) concluded for their data that “apparently, more training did not lead to greater reliability” and explained it by the fact that “the more a panel tries to find small differences between samples, the more attributes are liable to show a replicate effect, just because it is more difficult to describe the differences”. Our data suggested that panellist reliability may be determined not only by training but might also depend on the personal ability of subjects to concentrate. Further research is needed to confirm these preliminary findings.

CONCLUSIONS Relationships between selection and profiling performances were investigated among the selected panellists. Although a low coefficient of variation was observed on the Familiar Odour Recognition test scores (16%), this test was shown to be a potential screening evaluation tool for selecting discriminating panellists. Moreover, the olfactory memory estimated through the Recognition Memory for Odour test has a strong influence on the multidimensional discrimination power of the panellists. Finally, panellist reliability may be detected by a nonsensory test, evaluating the concentration ability. These results were obtained on eighteen subjects who passed the screening procedure; these data have to be considered as preliminary, although significant relationships have been observed. The real selection effect could only be studied by comparing the profiling performances of selected panellists and nonselected panellists. Such work is currently being undertaken by the authors.

ACKNOWLEDGEMENTS The authors would like to thank C. J. Wolters for the fruitful discussions on the screening and training of descriptive panellists and assistance in adapting the

The Future Performance

Verbal creativity test and the Bourdon T.I.B. test to the French language. This collaboration was supported by a FLAIR-SENS project. Pascal Schlich is warmly thanked for his enthusiastic interest for sensory data analysis and for his contribution to provide useful and original statistical tools to better interpret our data. Catherine Juffard and Pierre Lavier are thanked for their high quality technical assistance.

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I. & Issanchou,

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memory

familiarity

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