Sensory properties of hard cheese: Identification of key attributes

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1995 Elsevier in Ireland.

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09%6946;95:$9.51) ELSEVIER

Sensory Properties of Hard Cheese: Identification of Key Attributes

D. D. Muir,‘* E. A. Hunter,h “Hannah Scottish

Research Institute, Ayr, KA6 .5HL, UK

Agricultural

James Clerk Maxwell

(Received

J. M. Banks” & D. S. Horne”

Statistics Service, The University of Edinburgh, Building, The King’s Buildings, Mayfield Road, Edinburgh. EH9 352, UK

3 November

1993; revised version accepted

24 February

1994)

ABSTRACT The sen,sor_vproperties af‘ 16 samples crf‘hard cheese encompassing the muin types on sale in the United Kingdom have been studied. A panel of 16 assessors rated the cheese according to ,five odour, ten ,flavour and ,fi’ve textural attributes. The data were analysed h_v the Residual Mauimum Likelihood technique und estimates of @cts qf sample, order af tasting, carryover and assessor wre computed. Sign~fi’cant sample d(fftirences were ,found ,for 19 af’ the 20 attributes. Generalized Procrustes Analysis was also applied to the individual assessor matrices ,fbr odaur, ,flavnur and te.uture as a method qf‘ data simpltfi’cation that allows ,for d$f&eme.s between assessors. Consensus models were ,fi‘tted which uccounted ,fbr 41658% of the total variation after rotation and scaling. These models were af low dimensionalit_vand were used to construct perceptual space maps. Interpretation af the individual dimensions in terms of the originul attributes was achieved by correlation and projection methods. This allowed the ke? elements of’the scnsq~ properties qf’hard ehcese to be identified.

INTRODUCTION

Standards of identity for dairy products have become important with the creation of a genuine free trade area within the European Union (EU). Such product definition is particularly appropriate for hard cheese because it is widely traded throughout the EU. In the case of many dairy products, identity *To whom correspondence

should be addressed. 157

158

D. D. Muir et al.

can be established by compositional measurements but such a strategy has limited value for cheese. Although the composition of cheese may be readily fixed at the time of manufacture, the character of the product is defined by subsequent enzymic and chemical changes. Sensory characterization of cheese offers an alternative method of product definition but is by no means straightforward. Sample presentation, sensory vocabulary and the assessors must all be carefully controlled (McEwan et al., 1989; Muir & Hunter, 1992). Sample presentation is particular important with cheese (Muir & Hunter, 1991/2; Hirst et al., 1994) and may be estimated and nullified by appropriate experimental designs (MacFie et al., 1989; Muir & Hunter, 1991/2). Suitable vocabularies have been developed for use by individual panels for normal and low-fat Cheddar cheese (McEwan et al., 1989; Muir & Hunter, 1992) and also for normal and low-fat Norwegian cheese (Hirst et al., 1994). The problems associated with assessor response and reproducibility have been considered in detail (Piggott, 1988; Sinesio et al., 1991/2) and methods of ameliorating their effect by appropriate data analysis are well developed (see, e.g., Jack & Piggott, 1991/2). Cultural differences between assessors may be of lesser importance (Hirst et al., 1994). In a comparison of hard cheese by a Scottish and a Norwegian Panel, despite apparent differences in sensory acuity, sensory space maps constructed after Principal Component Analysis were very similar. The objective of the work reported here was to extend comparative studies of cheeses of different origins. In particular, a clarification of the key elements used for sensory classification of hard cheese was sought. There are literally hundreds of varieties of hard cheese available throughout Europe but it was impractical to consider a comprehensive study. Therefore, research was confined to hard cheese commonly available in UK retail outlets and, as a further simplification, mouldripened products were not considered.

MATERIALS

AND METHODS

Cheese samples Samples of hard cheeses were purchased from local retail outlets in Scotland. The samples were representative of the cheese range available locally and comprised (a) three samples of Cheddar cheese, differing in maturity (mild, mature and vintage), (b) six UK territorial varieties (Cheshire, Lancashire, Wensleydale, Caerphilly, Leicester and Gloucester), (c) four cheeses of Dutch origin (Edam, two variants of Gouda (normal and mature) and Maasdammer, (d) two Swiss samples (Emmental and Gruyere) and (e) a single Norwegian cheese (Jarlsberg). Care was taken to ensure that samples were within their ‘sell-by date’ and were from the same product batch. Samples were stored under refrigeration (4°C) until required, but were held at ambient temperature for about 1 h before sensory evaluation. Chemical composition The pH, moisture, crude protein and salt content of the cheeses were estimated as described by Banks et al. (1984).

Sensory properties

of hard cheese:

identification qf’ke,c attributes

159

Sensory assessment The sensory panel comprised 16 assessors drawn from a pool of 28 experienced and screened, but untrained, members of the Hannah Research Institute staff. All assessors had substantial previous experience of sensory evaluation of hard cheese. However, this experience was mainly limited to characterization of Cheddar varieties and few had substantial exposure to all the varieties being evaluated. For this reason, all cheeses were sampled by several highly experienced members of the panel in an unstructured way and, following a group discussion, it was agreed that the vocabulary developed for Cheddar cheese (Muir & Hunter, 1992) did not fully describe all samples. As a result, the modified vocabulary, detailed in Table I, was developed. This vocabulary included the attributes sweet flavour and rubbery texture. The panel was asked to rate each cheese on a 12.5 cm un-differentiated scale with the anchor points ‘absent’ and ‘very strong’ (Schiffman et al., 1981) and the rating was measured and transformed on a scale of O-100. Data were collected on paper forms rather than by our normal computer system because this was a special, non-routine study. Sensory evaluation was carried out in purpose-built sensory cubicles and assessors were instructed to consume a plain biscuit and to rinse their mouth with fresh, cold tap water before and between evaluation of samples. No time constraint was placed on assessors. Four samples were presented per session in a pre-determined order to allow order of tasting effects to be estimated (Williams, 1949; MacFie et al., 1989; Muir & Hunter 1991/2). The design was balanced for the effects of session, order of presentation within session, assessor and sample. Data analysis An analysis of each sensory variate was carried out by ‘analysis of variance’ using the Residual Maximum Likelihood (REML) technique (Patterson & Thompson, 1971; Horgan & Hunter, 1992) as implemented in Genstat 5

TABLE 1 Sensory Vocabulary Odour attributes

Odour intensity Creamy/milky odour Sulphur/eggy odour Fruity/sweet odour Rancid odour

Flavour attributes

Flavour intensity Creamy/milky flavour Acid/sour flavour Sulphur/eggy flavour Fruity flavour Rancid flavour Bitter flavour Animal/cowy/unclean flavour Salty flavour Sweet flavour

Textural attributes

Firmness Rubbery character Pasty character Grainy character Mouth-coating character

160

D. D. Muir et al.

(copyright 1992; Lawes Agricultural Trust, Rothamsted Experimental Station, Harpenden, Herts). Random effects were fitted for assessor and fixed effects for order of tasting, cheese sample and for the carryover effect of the previous sample. In recent studies we have identified important sources of betweensample variation by Principal Component Analysis (PCA) of the cheese effects from REML (Muir & Hunter, 1992; Muir et al., 1992). Such simplification is appropriate where the panel of assessors is familiar with the samples being assessed and where the level of ambiguity between sensory terms is controlled. However, in the present study these conditions were not fully met. As a result, multi-variate analysis of the data was carried out using Generalised Procrustes Analysis (GPA) (Gower, 1975; Dijksterhuis & Gower, 1991/2) as implemented in Genstat 5. For a description of the technique in the context of sensory testing of food, see Arnold and Williams (1986). Such treatment assumes that the assessors perceive the samples in the same way in sensory space, but that they may use different descriptors to assess a common attribute and that they may use different locations and proportions of the scale. As a result of transformation and scaling, interpretation of the dimensions derived from GPA is less straightforward than with PCA. This problem was resolved by considering the regressions of the scores (on the major sensory dimensions) on the individual attribute ratings for the sample effects derived from the REML analysis. In addition, the projection technique recently advanced by Arnold and Collins (1993) was also evaluated. Before accepting the consensus from GPA, it is wise to check (a) the agreement between assessors and (b) the goodness of fit of the analysis. The agreement between assessors was checked by forming a matrix of similarities (after Sinesio et al., 199lj2) which was then analysed using Hierarchical cluster analysis. In order to find a consensus, GPA applies a vigorous transformation of the data. Scepticism has been expressed on the validity of the consensus achieved. However, Wakling et al. (1992) have proposed a new test based on randomization. The treatment labels for each assessor are randomized and a GPA analysis performed. The percentage variation accounted for by the consensus after rotation and scaling is saved. The distribution of values allows the goodness of lit of the analysis on the data collected to be tested.

RESULTS Composition

of cheese

The composition of the 16 samples is shown in Table 2. These was a wide range in composition. For example, moisture content ranged from 33% (Gruyere and Vintage Cheddar-of New Zealand origin) to over 43% (Caerphilly and Edam). Equally large differences in pH (Range 4.98-6.02) and salt content (O&2.6%) were also apparent. Consensus configuration

The cluster analyses of the similarities between assessors showed that the assessors were in good agreement, with no outliers.

Sensory properties

qf hard cheese: ident$cation

qf’kq

161

attrihutes

TABLE 2 Composition of Cheeses CO&

Sample

Moisture (74)

Salt (96)

Protein (96)

PH

Chd Chd(m) Chd(v) Chs Lan Wen Cae Lei Cl0 Eda Gou Gou(m) Maa Jar Emm Gry

Cheddar, mild Cheddar, mature Cheddar,vintage Cheshire Lancashire Wensleydale Caerphilly Leicester Gloucester Edam Gouda Gouda, mature Maasdammer Jarlsberg Emmental Gruyere

37.0 36.6 33.4 39.5 40.0 39.9 43.4 36.4 38.1 43.5 40.9 40.5 41.2 41.1 38.4 33.3

2.2 I.9 I.7 I.3 I.5 I.7 I.2 I.6 I.9 2.6 2.5 2.6 I.0 I.1 0.6 I.5

24.7 25.6 24.5 23.6 22.4 22.0 22.0 244 22.8 25.3 22.6 24.5 26.4 27.4 26.9 27.5

5.60 5.52 5.36 4.98 5.02 5.03 5.05 5.54 5.30 5.26 5.56 5.44 5.88 5.66 5.65 6.02

Sensory properties-odour The sample effects and standard errors in the difference of the means (SED) for the five individual odour attributes are shown in Table 3. Significant differences between samples were noted in every case. To clarify the nature of these differences, GPA was carried out on 15 individual assessor matrices (one data set contained missing values and was thus discarded). After scaling and rotation, a consensus configuration was derived which explained 40.8% of total variance compared with a mean of 32.7% from simulation (the 99% critical value was 34.6%). Inspection of the partition of variance between dimensions in the consensus configuration suggested that a three-dimensional model was appropriate. The first three dimensions of the model apportioned 55.0, 19.1 and 14.6% of the fitted variation, respectively. As a result of the complex transformations involved in GPA, a simple interpretation of the dimensions in terms of the original attributes is not available. Two methods were employed here to interpret the sensory dimensions. First, the correlation of the scores on the first three dimensions with the attribute ratings from REML were considered (Table 4). The scores on the first dimension were highly correlated with overall odour intensity (0.95) and rancid odour (0.92) and, negatively, with creamy odour (-0.80). There were no high correlations between the scores on the second and third dimensions and the individual attributes, but the second dimension could be interpreted in terms of fruity/sweet odour (correlation -0.45) and the third dimension in terms of creamy/milky odour (correlation -0.43). Arnold and Collins (1993) have tackled the problem of interpretation of transformed axes in a different way. Essentially, their technique involves projection of

162

D. D. Muir et al. TABLE 3

Odour Attributes of Cheese. Estimated Sample Effects and Standard Error in Difference of Means from REML Analysis Sample

Estimated sample ejfect ,for odour attribute (range O-100) Odour intensity

Creamy/milky

34.9 45.5 55.7 36.3 34.3 28.9 38.0 36.0 34.0 49.0 38.7 48.0 45.6 46.1 58.2 60.6 6.2

20.0 12.4 12.3 23.5 15.1 15.8 22.9 14.5 18.6 I.2 17.7 17.2 14.4 20.6 IO.1 9.1 4.7

Cheddar, mild Cheddar, mature Cheddar, vintage Cheshire Lancashire Wensleydale Caerphilly Leicester Gloucester Edam Gouda Gouda, mature Maasdammer Jarlsberg Emmental Gruyere SED (max)

Sulphurleggy

6.4 10.8 18.3 6.3 5.3 4.9 6.1 7.0 7.9 1I.8 8.3 14.9 10.9 8.0 13.0 11.2 3.5

Fruity/sweet

Raneid

9.6 9.3 4.3 4.4 7.2 4.1 I.4 8.7 6,3 5.4 12.3 1.3 IS.4 12.6 9.7 16.4 6.1

10.6 20.7 28.5 15.5 7.7 6.4 13.2 3.4 4.2 23.1 12.1 22.7 19.8 11.7 29.8 36.2 7.0

the individual assessor transformed space onto the consensus dimensions from the Procrustes treatment. Projections were calculated from the fitted matrices and are shown in Fig. 1 as CUSUM diagrams for each of the I5 assessors (Arnold and Collins, 1993). These diagrams provide valuable information not given by the correlations because they not only identify the key attributes reflected in each dimension of the GPA but they also provide a measure of the agreement between assessors. Dimension 1 of the consensus analysis for odour could be attributed to a combined effect of overall odour intensity and to rancid odour (Fig. 1). In both TABLE 4

Correlation

of Cheese Scores and Main Odour Dimensions Derived from Procrustes Analysis with Original Attribute Ratings

Attribute

Correlation

qf score

Dimension 1

Intensity of odour Creamy/milky odour Sulphur/eggy odour Fruity/sweet odour Rancid odour Variance explained (%)

0.95 -0.80 0.45 0.40 0.92 55.1

Emboldened values are thought to be important

on dimension with attribute rating Dimension 2

0.04 -0.10 0.35 -0.45 0.01 19.1 contributors

Dimension 3

-0.09 -0.43 0.12 -0.04 -0.12 14.6 to score.

Sensory properties of hard cheese: identification qfkey attributes

163

(a) Dimension 1

0.4

0.1

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9

10

11

12

13

14

15

(b) Dimension 2

o.61 -... o.4

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.___._._._._._._._......................._

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i

0.2

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(c) Dimension 3 0.5

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . __

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10

11

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13

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Fig. 1. Singular-value CUSUM diagrams showing the dependence of the first three odour dimensions on the original variables (odour attributes) for all assessors after Generalized Procrustes Analysis, using fitted (smoothed) projections.

164

D. D. Muir et al,

cases, these original attributes featured in the projections of I3 of the 15 assessors. Creamy odour was less important, but was used by 12 assessors. Therefore, the interpretation derived from the correlations (Table 4) are soundly based. It had been noted in Table 4 that the correlations for scores on the lower dimensions were weak. This was reflected in the relevant CUSUM diagrams (Fig. 1). Although the projections were variable between assessors, fruity/sweet odour was a feature of the projections for 11 assessors and was present, albeit at a low level, in a further two diagrams. Interpretation of Dimension 3 was not clear. Although 12 assessors used creamy/milky odour to describe this attribute, six assessors employed the term sulphur/eggy. In conclusion, only Dimensions 1 and 2 can be interpreted with confidence. Dimension 1 was a contrast between, on the one hand, rancid odour and overall odour intensity and, on the other, creamy odour. Dimension 2 was associated with a fruity/sweet odour. A sensory space map of the consensus configuration was constructed for Dimensions 1 and 2 (Fig. 2). Separation of the cheeses on Dimension 1 was substantially greater than that on Dimension 2. Three clusters of samples could be discerned. The two Swiss cheeses, Gruyere and Emmental, were characterised in terms of their fruity/sweet/rancid character, whilst the six British territorial varieties (Wensleydale, Cheshire, Lancashire, Caerphilly, Leicester and Gloucester) and the sample of mild Cheddar cheese were closely clustered at the extreme end of Dimension 1. This result could be interpreted as meaning that the samples were not perceived as rancid in character. The other samples fell into a diffuse group, but it is worthy of note that the mature and vintage Cheddar cheese was perceived to have little fruity/sweet character, in contrast to the Dutch and Norwegian samples (Edam, Gouda, Maasdammer and Jarlsberg). Sensory properties-flavour Sample effects and SEDs from the REML analysis of the flavour attributes are shown in Table 5. With the exception of the ratings for fruity flavour, all other attributes differed significantly between cheese samples. The salty flavour of the cheese was clearly determined by salt content (Table 2) and a very highly significant relationship (r = 0.87; p < 0.001) was found. In contrast, perception of acidity was not related to the pH of the cheese (r = 0.27; not significant). The individual assessor matrices for cheese flavour were then analysed by GPA. In this case, the consensus configuration explained 57.2% of the total variance compared with a simulation mean of 46.2% (the 99% critical value was 47.5%). Of the variance modelled, over 80% was explained by four dimensions accounting for 41.5, 18.3, I 1.3 and 9.5%, respectively. The correlation between the scores on Dimensions l-4 and the attribute ratings from REML (Table 5) are shown in Table 6. Interpretation of the first dimension suggested that this was perceived in a complex way. The scores were highly correlated with cowy flavour (0.98), overall flavour intensity (0.85), rancid flavour (0.83), sulphur/eggy flavour (0.80) and, negatively, with creamy flavour (-0.94). In contrast, the scores on Dimension 2 were positively correlated with salty flavour (0.73) and negatively correlated with sweet flavour (-0.64). An equally straightforward interpretation of Dimension 3 was also possible because the scores were negatively correlated with acid-sour

Sensory properties qf hard cheese: identijication ofkey uttrihutes

165

0.3

Chdlvl

-0.2

L

0

-0.2

Dimension

0.3

2 (-Fruity/Sweet)

Fig. 2. Sensory space map for odour attributes of cheese constructed from scores on first and second dimensions of the consensus configuration derived by Generalized Procrustes Analysis of individual assessor matrices. For code, see Table 2.

flavour (-0.74) and bitter flavour (-0.67). Scores on Dimension 4 were not highly correlated with any single character. Nevertheless, these was a weak correlation with sulphur/eggy flavour (-0.40). Inspection of the regression suggested that, if the score for Emmental cheese was excluded, this relationship became strong (Y = -0.83). Once again, CUSUM diagrams of the individual assessors’ projections onto the consensus configuration were constructed (Fig. 3(a) and (b)). Ten of the 15 assessors used animal/cowy/unclean flavour as an important descriptor associated with flavour Dimension 1. Although overall flavour intensity was used by more assessors (12), the CUSUM diagrams confirmed that it was of less importance than cowy flavour. A similar comment applied to creamy flavour which contributed to the projections of 14 assessors. In contrast, whilst the correlations for eggy and rancid flavour (0.80 and 0.83, respectively) suggested a contribution to flavour Dimension 1, the CUSUM diagrams revealed that these attributes

TABLE 5

Cheddar, mild Cheddar, mature Cheddar, vintage Cheshire Lancashire Wensleydale Caerphilly Leicester Gloucester Edam Gouda Gouda, mature Maasdammer Jarlsberg Emmental Gruyere SED (max)

Sample

29.2 23.5 20.3 31.2 27.9 30.5 33.3 27.1 37.0 33.4 36.2 33.1 22.1

42.0 65.8 76.0 49.0 42.9 47.3 46.8 464 36.4 48.7 44.4 56.5 53.9 53.4 66.5 72.3 6.1

27.6 8.9 17.6 5.5

Creamy/ milky

Flavour intensity

19.7 17.5 7.9 13.2 2.6 9,4 3.9 5.1 28.6 24.6 6.6

23.2 22.2

19.6

6.8 18.8 23.9

Acid/sour

7.7 10.0 16.8 3.6 4.8 1.8 4.7 4.4 5.7 4.2 8.1 10.5 14.0 13.6 13.4 19.7 4.6

eggy

Sulphur/

7.4 7.3 7.2 3.5 9.9 5.5 10.8 4.5 9.0 5.8 6.0 4.5 7.3 5.6 7.4 4.9

8.9

Fruity

4.2 13.7 25.0 14.5 14.2 10.6 19.4 6.2 4.2 11.1 7.6 12.7 24.1 15.4 22.0 29.5 6.5

Rancid

3.4 10.0 11.1 11.7 8.5 10.7 11.4 18.2 8.3 4.0 1.6 4.5 2.6 9.6 12.0 11.9 5.3

Bitter

6.5 20.4 28.6 14.0 10.4 3.8 11.5 7.9 9.2 10.3 4.0 6.7 21.5 18.8 47.0 43.6 6.7

Animaljcowyl unclean

Estimated sample effect forf’lavour attributed (range O-100)

6.7 3.2 2.5 7.8 13.2 4.1 12.0 1.1 4.6 0.2 7.2 3.0 24.4 22.4 19.4 15.0 5.9

14.5 10.9 18.4 21.6 33.3 27.8 32.5 13.7 14.6 Il.0 15.5 5.4

Sweet

18.1 25.0 17.2 12.8 8.2

Salty

Flavour Attributes of Cheese. Estimated Sample Effects and Standard Error of Difference of Means from REML Analysis

$j T 2 %

P !=’

Sensory properties

Correlation

qfhard

cheese: ident$cation

qf‘key

167

attributes

TABLE 6 of Cheese Scores on Main Flavour Dimensions Derived from Procrustes Analysis with Original Attribute Ratings

Attribute

Correlation Dimension

Intensity of flavour Creamy/milky flavour Acid/sour flavour Sulphur/eggy flavour Fruity flavour Rancid flavour Bitter flavour Animal/cowy/unclean flavour Salty flavour Sweet flavour Variance explained (%)

1

of score on dimension

Dimension

04%

2

with attribute

Dimension

3

rating

Dimension

-0.94 0.52 0.80 -0.14 0.83 0.34 0.98

0.36 0.01 -0.12 0.18 0.39 -0.14 -0.18 -0.01

-0.17 0.06 -0.74 0.30 -0.26 -0.11 -0.67 0.03

-0.20 -0.12 0.23 -0.40 0.11 -0.36 0.23 0.02

-0.38 -0.53 41.5

0.73 -064 18.3

0.35 0.42 I I.3

0.05 -0.16 9.5

Emboldened values are thought to be important

contributors

4

to score.

were important

to only a small number of assessors. As a result, the interpretation of flavour Dimension 1 may be simplified to a contrast between animal/ cowy/unclean flavour and creamy flavour. Examination of the relevant CUSUM diagram (Fig. 3(a)) suggested that

flavour Dimension 2 was associated with the contrast between fruity/sweet and creamy flavour and salty flavour. This conclusion is compatible with that from the correlations of scores with original attributes (Table 6). In a similar way (Fig. 3b), flavour Dimension 3 was associated with acid flavour (12 assessors). However, the contribution of bitter flavour (8 assessors) was weaker and the CUSUM diagrams suggested that salty flavour made a contribution equal to bitter, although this was not clear from examination of the correlation (Table 6). As expected from the weak correlations (Table 6) flavour Dimension 4 could not be strongly associated with any original attribute. Nevertheless, examination of the relevant CUSUM diagrams (Fig. 3(b)) suggested that acid (10 assessors) and eggy (8 assessors) flavours were associated attributes. Sensory spacemaps for Dimensions 1 and 2 and Dimensions 3 and 4 were constructed, as before (Figs 4 and 5, respectively). The cheeses partitioned into four clusters on Dimensions 1 and 2. Separation was greatest on Dimension 1 (creamy versus cowy character), but was also substantial on Dimension 2 (salty/ sweet and creamy contrast). For example, the Swiss cheeses (Emmental and Gruyere) were distinctively separated from the British territorial variety (Caerphilly, Lancashire, Cheshire, Wensleydale, Leicester and Gloucester) in terms of creamy contrasting with cowy character. On the other hand, Dimension 1 did not separate the British territorial varieties from the Dutch cheese or the mature and vintage Cheddar from the Jarlsberg and Maasdammer. However, these samples were separated by Dimension 2. Separation on Dimensions 3 and 4 was less

et al.

D. D. Muir

168 Dimension

1

9

12345678

10

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13

14

15

ASSeSSOr

Dimension 2

0

1

2

3

4

q

Intensity

0

Fruity

q q

w

Salty

LYS w-et

5

6

Creamy/milky Rancid

7

8

0

9

101112131416

Acid/sour

&8 Sulphurleggy

Bitter

8

cowy

Fig. 3a. Singular-value CUSUM diagrams showing the dependence of (a) flavour dimensions 1 and 2 on the original variables (flavour attributes) for all assessors after Generalized Procrustes Analysis, using fitted (smoothed) projections.

(Fig. 5). There appear to be two main clusters with the Swiss cheeses as outliers but markedly separated by the scores on Dimension 4. Although Dimension 4 reflected acid/eggy flavour, it should be noted that the Emmental sample was an outlier from the regression for eggy flavour. One interesting feature of the Dimension 3/4 sensory map is the separation of the British territorial and Cheddar cheeses from the other varieties on the basis of acid flavour. Gloucester cheese was the only exception to this trend.

complex

Sensory properties-texture

The sample effect and SEDs from the REML analysis of the textural attributes are shown in Table 7. All five attributes showed significant variation between

Sensory properties qfhard cheese: ident$cation qf’key attributes

169

Dimension 3

Dimension 4

q Creamy/milky q Rancid

0

Acid/sour

q Sulphurleggy

Bitter

R

cowy

EZIS weet

Fig. 3b. Singular-value CUSUM diagrams showing the dependence of (b) flavour dimensions 3 and 4 on the original variables (flavour attributes) for all assessors after General-

ized Procrustes Analysis, using fitted (smoothed) projections. samples. In particular, the range of values for rubbery character (0.2-68.4) and pasty character (13.8-S 1.O) was large. GPA was then carried out on the individual assessor matrices. The consensus configuration explained 58.5% of the total variation, compared with the mean value of 34.8% from simulation (99% critical value was 38.0%). Of the modelled variation, 57.4, 25.1 and 9.9% of the variation was accounted for by Dimensions 1, 2 and 3, respectively. The correlation of the cheese scores on each of these dimensions with the original attribute scores from the REML analysis (Table 7) is shown in Table 8. The scores on Dimension 1 were highly correlated with rubbery texture (0.87) and, negatively, with grainy (-0.86) and pasty (-0.77) character. Attribution of Dimension 2 scores was less certain, but significant correlations

D. D. Muir et al.

170

-0.2

0 Dimension

0.3 2 (Salty/-Sweet)

Fig. 4. Sensory space map for flavour attributes of cheese constructed from scores on first and second dimensions of the consensus configuration derived by Generalized Procrustes Analysis of individual assessor matrices. For code, see Table 2. with mouth-coating texture (0.70) and pasty texture (-0.61) were found. Dimension 3 reflected the perceived firmness of the samples (r = O-90). These conclusions were altered slightly by consideration of the assessor projections (Fig. 6). Whilst no change would be made in this interpretation of texture Dimensions 1 and 3, there was evidence that the correlation overestimated the contribution made by mouth-coating character to this dimension. The attribute was only featured in the projections of four assessors. A contrast between rubbery and pasty character was an interpretation more consistent with the CUSUM diagrams (Fig. 6). Sensory space maps were constructed for texture using the scores on Dimensions 1 and 2 (Fig. 7) and 1 and 3 (Fig, 8). There was some evidence of clustering on the Dimension l/2 map with a tight group of samples exhibiting marked rubbery character. The Norwegian and Dutch cheeses fell into this category, but it also included the Emmental cheese and the mild Cheddar sample. The remaining samples exhibited less rubbery character and might be regarded as displaying

Sensory properties of hurd cheese: identtfication

qf’key attributes

171

0.2

Emm

0

-0.15

J

-0.15

0

0.2

Dimension 4 (-Eggy) Fig. 5. Sensory space map for flavour attributes of cheese constructed from scores on third and fourth dimensions of the consensus configuration derived by Generalized Procrustes Analysis of individual assessor matrices. For code, see Table 2.

a continuous range of pasty/crumbly texture. At one extreme, Lancashire and Wensleydale cheese were perceived as very pasty, whilst at the other, Leicester and Gloucester were neither pasty nor particularly rubbery. Although separation on Dimension 3 (firmness) was less clear, two samples, Gruyere and vintage Cheddar, lay at extreme values. These samples were distinguished by having very low moisture contents (c. 33%).

DISCUSSION Sensory analysis has identified the main characteristics used by a sensory pane1 to distinguish between cheeses of widely different types. Analysis of variance by REML identified five odour, nine flavour and five textural attributes which

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D. D. Muir et al.

TABLE 7 Textural Attributes of Cheese. Estimated Sample Effects and Standard Error of Difference of Means from REML Analysis Sample

Estimated sample effect for textural attribute (range O-100)

Cheddar, mild Cheddar, mature Cheddar, vintage Cheshire Lancashire Wensleydale Caerphilly Leicester Gloucester Edam Gouda Gouda, mature Maasdammer Jarlsberg Emmental Gruyere SED (max)

showed significant from fruity flavour

Firm

Rubbery

Pasty

Grainy

Mouth coating

41.1 38.1 68.7 38.6 41.6 36.6 27.4 34.8 25.2 52.0 40.9 54.8 47.0 47.0 35.6 67.4 6.8

33.0 6.4 8.0 7.4 2.2 4.9 0.2 14.2 12.7 38.2 57.2 47.7 68.4 56.2 52.5 22.7 7.6

23.3 24.1 50.6 50.8 81.0 78.7 48.0 16.0 13.8 34.2 22.3 36.7 38.8 36.6 35.2 35.7 8.5

16.4 19.5 36.0 44.9 60.8 52.2 43.8 15.2 8.2 22.9 17.0 23.9 23.1 24.1 23.7 22.7 7.1

31.8 50.3 48.4 36.7 26.6 33.5 32.4 46.3 44.7 35.6 36.8 28.7 34.5 29.0 37.5 49.2 6.4

differences between were not significant).

samples

(differences

between

samples

REML analysis has the advantage that first-order carryover and order of tasting effects can be allowed for. Some allowance is also made for assessor effects by taking into account the location of the means of each assessor. Nevertheless, this analytical technique neither allowed for differences in interpretation of attributes nor for differences in

Correlation

TABLE 8 of Cheese Scores on Main Textural Dimensions Derived from Procrustes Analysis with Original Attribute Ratings

Attribute

Firmness Rubbery Texture Pasty character Grainy texture Mouth-coating texture Variance explained (%)

Correlation of score on dimension with attribute rating Dimension 1

Dimension 2

Dimension 3

0.12 0.87 -0.77 -0.86 0.04 57.4

-0.27 -0.49 -0.61 -0.49 0.70 25.1

0.90 -0.03 0.07 -0.05 0.48 9.9

Emboldened values are thought to be important

contributors

to score.

Sensory properties

qf’hard

cheese: ident(ficution

of’kq

173

uttrihutrs

(a) Dimension 1

0.5 a

0.4

g ZE 0.3 2 s

0.2 0.1 0

12345678

(b) Dimension 0.6

9

10

11

12

13

14

9

10

11

12

13

14

15

2

0~5.__________

a 3 F I

0.4 0.3

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0.2 0.1 0

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(c) Dimension

3

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.

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15

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0

9

12346679

10

11

12

13

14

15

Assessor OFirmnesa

aRubbery

OPsaty

=Grainy

~Mouth-coating

)

Fig. 6. Singular-value CUSUM diagrams showing the dependence of the first textural dimensions on the original variables (textural attributes) for all assesors Generalized Procrustes Analysis, using the fitted (smoothed) projections.

three after

D. D. Muir et al.

174

Gou

Maa

J”E mm

Ed Gou %?hd

i

Chdfvl

Wen

I

Lan

-0.3

0 Dimension

0.3

2 (Mouth-coating/-Pasty)

Fig. 7. Sensory space map for textural attributes of cheese constructed from scores on first and second dimensions of the consensus configuration derived by Generalized Procrustes Analysis of individual assessor matrices. For code, see Table 2. scaling. Arnold and Williams (1986) have suggested that Generalized Procrustes Analysis (GPA) can be applied to adjust for such variation and McEwan et al. (1989) successfully applied this technique to distinguish between seven samples of Cheddar cheese. In the present study, the CPA consensus successfully modelled 40.8, 57.2 and 58.5% of the total variation (after rotation and scaling) for odour, flavour and texture, respectively. Moreover, GPA provided a consensus model of low dimensionality-three dimensions explained 88.8% and 92.3% of the modelled variance for odour and textural attributes, respectively. A model with four dimensions, which together explained 80.6% of the modelled variance, was thought to be more appropriate for flavour attributes. Using the scores derived from the consensus configurations of the individual GPAs for odour, flavour and texture, sensory space maps were constructed which indicated the relative positions of the cheeses in sensory space. Comparison of these maps (Figs 2, 4, 5, 7 and 8) clearly indicates that odour, flavour and texture

Sensor!~ properties

of’hurd

cheese: identification

of’ke?

attributes

175

I

Chdlvl

Cae

Chs

La

_

c

0 Dimension

0.3

3 (Firmness)

Fig. 8. Sensory space map for textural attributes of cheese constructed from scores on first and third dimensions of the consensus configuration derived by Generalized Procrustes Analysis of individual assessor matrices. For code, see Table 2.

should be considered separately. Although we have previously considered the sensory character of Cheddar cheese in terms of overall properties (i.e. odour, flavour and texture together) (Muir and Hunter, 1992) this may have led to a biased view and there may be value in considering the different aspects of sensory characters of cheeses independently. GPA is a powerful technique for deriving consensus configuration but, as a consequence of the manipulations inherent in the process, interpretation of the dimensions in terms of the original attributes is difficult. We have adopted two strategies to overcome this problem. First, we considered the correlations of the scores for each on the individual dimensions of the GPA with the REML estimates of individual attributes. Significant correlations were found in most cases. This finding allowed an interpretation to be made. Second, we adopted a more rigorous approach using the technique recently proposed by Arnold and Collins (1993). They suggested that a valuable indication of the relationship between the

176

D. D. Muir

Texture

First Dimension

et al.

Flavour

rubbery I+ I

cowyhnclean

pasty l-J

creamy

I-l

grainy I-I

rubbery

Second Dimension

Third Dimension

Fourth

Fig. 9.

I-)

sweet

(-1

Odour I+J

intensity(+) rancid

(+)

creamy

I-I

fruity/sweet

I-I

pasty1+1

Firmness

I+)

Acid

I-I

Dimension

Hierarchy of sensory attributes used for discrimination of cheese.

dimensions and the original attributes could be derived by projection of the transformed individual assessor space on the consensus configuration. This technique has additional merit in that it provides information on the differences in interpretation between assessors. Both methods gave similar interpretations to the consensus configurations. Comparison of the important dimensions with those in McEwan et al. (1989) reveals a reassuring degree of overlap, despite the substantial differences in the methodology used and in the cheese samples considered. McEwan et al. (1989) identified the contrast between creamy/milky character zind flavour strength and manure flavour as the main contribution to Dimension 1. In this work, very similar terms were attributed to the first dimension. McEwan et al. (1989) were less definite about interpretation of the contribution of flavour to the second dimension but identified acid flavour. We found that salty/sweet contrast was important, but such differences would be slight within the types of cheese examined by McEwan et al. (1989). Nevertheless, there is further substantial agreement in terms of texture, for both the present study and that of McEwan et al. (1989). Both studies identify grainy and rubbery character as key descriptors. From the finding of this work it is feasible to construct a sensory key for cheese based on selected characteristics (see Fig. 9). Such definition of the sensory properties of cheese will enhance opportunities for trade and provide a useful tool for underpinning scientific and technological research. ACKNOWLEDGEMENTS Mrs C. Shankland and Miss E. Noble are thanked for their expert technical assistance. This study was funded by The Scottish Office, Agriculture and Fisheries Department.

Sensory properties of’ hard cheese: ident(fi’cation af’key attributes

177

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