Comparison of Dynamic Flavour Release from Hard Cheeses and Analysis of Headspace Volatiles from the Mouth with Flavour Perception during Consumption

J Sci Food Agric 1996,71,273-281

ComDarison of Dvnamic Flavour Release from I J

Hard Cheeses and Analysis of Headspace Volatiles from the Mouth with Flavour Perception during Consumption* Conor M Delahunty,$ John R Piggott, John M Conner and Alistair Paterson Centre for Food Quality, University of Strathclyde, Department of Bioscience and Biotechnology, 204 George Street, Glasgow G1 lXW, UK (Received 30 March 1995; revised version received 4 December 1995; accepted 26 January 1996)

Abstract: Flavour compounds are released at different times and with differing rates during cheese consumption. This aspect of flavour has received little atten- tion, although it is likely to be of great importance in the perception of hard cheese flavour. Full-fat and reduced-fat hard cheeses were anal ysed by standard buccal headspace methodology and by conventional descriptive sensory analysis. Cheeses were further analysed by ‘time-concentration’ buccal headspace methodology and sensory time-intensity analysis. Conventional data distinguished between individual cheeses and between fat contents. Time-course data confirmed the results of conventional analysis, but also allowed the examination of release behaviour and changes in flavour perception with time. Partial least squares regression was successful in predicting some sensory attributes from standard buccal extracts. Time-course studies improved predictive ability.

Key words: Cheddar cheese, flavour, buccal headspace, descriptive analysis, time-intensity, principal components analysis, partial least squares regression.

INTRODUCTION

The flavours of hard cheeses are derived from the balance of odourants and taslants released during their consumption. These compounds interact with human receptors initiating a behavioural response that is the cheese flavour (von Sydow 1971). A primary concern of cheese flavour research is to determine which compounds are important, and how those compounds are balanced with each other. Many difficulties are encoun- tered when attempting to do so. Flavour compounds present in cheese have different physicochemical properties. Secondly, cheeses are complex biological systems and as such they exist in a constant state of change in accordance with their physical environment. The human aspect also complicates, as consumers vary in

their perceptions and expressions of flavour (Lancet 1992; Piggott 1994). These facts are important when attempting to obtain an understanding of cheese flavour.

Because a ‘bite-sized’ piece of cheese may take 15- 30 s to consume (Shamil et a1 1992), the cheese’s physical composition, and the consumer’s mouth environment, rate of chew and time of swallow all influence release of flavour compounds (Overbosch et a1 1991). A great variety of compounds is released at different times and rates during consumption, but the number of chemical stimuli which a person can perceive and identify at a given moment is small (Laing 1994). Therefore, one could conclude that different flavour attributes are perceived at different times.

Fat content is a compositional factor which could influence flavour release. Fat concentration is likely to affect not only the maximum intensity of sensations, but

pation (Gurr and Walstra 1989). Accordingly, important effects of ComPosition on flavour Perception may be overlooked by neglecting the temporal aspect of

* This Paper was Presented in Part at Bioflavour’95, DiJOn, also their temporal rates of development and dissi- France. 1 To whom correspondence should be addressed at: Depart- ment of Nutrition. University College Cork, Republic of Ireland.

J Sci Food Agric 0@22-5142/96/$09.0@ 0 1996 SCI. Printed in Great Britain 273

274 C M Delahunty et a1

flavour release and collecting compositional data and sensory intensity ratings at a single time point.

The work described here was carried out to compare four methods used to analyse cheese flavour. Buccal headspace methodology (Delahunty e f a1 1994) provided data describing the total of the volatiles released in the mouth, and an adaption of this method provided compositional data during the course of cheese consumption (temporal buccal headspace). Conventional descriptive sensory analysis, and time-intensity descriptive analysis (Delahunty et a1 1996) provided sensory data. The relationships within and between data sets from these methods were examined to test whether dynamic aspects of flavour release are important in explaining cheese flavour.

EXPERIMENTAL

Standard buccal headspace

Eight mature Cheddar-type cheeses, four traditional and four reduced in fat content, were purchased locally. In duplicate, volatiles from a 50 g sample of each cheese were analysed by buccal headspace analysis (Delahunty et a1 1994). Each sample was divided into 10 x 5 g 'bite- sized' pieces and the entire sample consumed in a natural fashion over a 5 min period, ie 30 s per cheese piece (at a timed rate of one bite per second). Headspace released during consumption was displaced from the buccal cavity, via the nose, with a vacuum pump (Speedivac High Vacuum Pump, Associated Electrical Industries Ltd, Newcastle, Staffs, UK) at a rate of 60 f 10 ml min-' and concentrated on a precondi- tioned Tenax-TA precolumn (Chrompack UK Ltd, London, UK). Blank buccal headspace, which consisted of 5 min trapping without cheese consumption, was taken each morning and after lunch. Blanks were also taken to determine volatiles remaining in the mouth after cheese consumption. Replicates were consumed on different days and in different order.

The Tenax-TA traps were desorbed thermally using a Purge and Trap Injector control unit (Chrompack). Trapped volatiles were swept from the traps with oxygen-free nitrogen (25 ml min-') for 2 min at 250°C. The volatile compounds were trapped in the cold trap at - lOO"C, and desorbed at 230°C for 5 min. Desorbed volatiles were analysed by gas chromatography-mass spectrometry (GC-MS) with a Finnegan-MAT ITS-40 (Finnegan-MAT, Heme1 Hempstead, UK). The column was a 30 m x 0.25 mm Carbowax BP52 (df = 0.25 pm) (Chrompack) programmed from 40°C (5 min) to 80°C at 3°C min-' then to 220°C at 30°C min-'.

Temporal buccal headspace

Four Cheddar-type cheeses, two full-fat and two reduced in fat, an arbitrary subset of the eight above

selected at time of purchase, were assessed by temporal methodology. In duplicate, buccal headspace extract was continuously collected, as above, for four time periods (15, 30,45 and 60 s) of cheese consumption. For experimental practicality each 'bite-sized' cheese piece was consumed at half the previous rate (one bite per 2 s), but the same total weight of cheese was used (10 x 5 8). After the elapsed time period, for each of the cheese pieces, the gas flow to the Tenax-TA trap was closed, the cheese piece spat out and the mouth rinsed with deionised water. For the 60 s sample each cheese piece was swallowed before rinsing. Swallow times were recorded. Regular monitoring of blank buccal headspace ensured that carry-over was negligible. Replicates were consumed on separate days and cheeses were sampled in different order on these days. Desorption of traps and GC-MS analysis used the same column and conditions as above. One person consumed all buccal headspace samples. This person was male, aged 26 years, in good health and non-smoking.

Conventional descriptive sensory analysis

The same eight cheeses were assessed in triplicate, using a vocabulary of 14 descriptors developed for Cheddar cheese flavour analysis (Delahunty et al 1996), by 14 trained assessors. Samples were presented as 5 g cubes in glasses, covered by watchglasses. Eight samples, in random order, were presented per session. Procedure followed that used previously (Delahunty et al 1996). Data were collected using the PSA-System (Oliemans, Punter and Partners, Utrecht, The Netherlands).

Timeintensity sensory assessment

Four discriminating descriptors were chosen from the Cheddar vocabulary after processing the descriptive analysis results. The four cheeses assessed by temporal buccal headspace were subjected to time-intensity (T-I) sensory assessment. Nine (of the 14) trained panellists assessed the cheeses, in duplicate, for each of the four descriptors (five assessors were not available for all analyses). Four cheeses and one descriptor were presented per session. A Williams square design (Williams 1949) was followed for sample presentation and the order of descriptors was varied for the duplicate analysis. Attribute intensity ratings were continuously scored using the T-I module of the PSA-System, collecting 1 data point s-I . Swallow times were recorded during consumption. Presentation and procedure details were similar to those used for descriptive analysis.

Statistical analysis

Analyses of variance (ANOVA) were carried out using Minitab release 9. Conventional data matrices, instru-

Flavour release behaviour from hard cheeses 275

1 0.4 I I

*- .,,a".". L,.....

-0.3 -0.4 -0.2 0.0 0.2

Principal component 1

Fig 1. Principal component scores for eight cheeses and loadings of compounds from PCA of standard buccal headspace data. The 95% confidence interval is shown, calculated from

duplicate ainalyses.

mental and sensory, were analysed by principal components analysis (PCA; Piggott and Sharman 1986) using the Unscrambler v 5.5 (CAM0 A/S, N-7041 Trondheim, Norway) and related using partial least squares regression (PLS; Martens and Martens 1986) with the Unscrambler. Numbers of components to use in all PLS analyses were chosen by cross-validation. Descriptive analysis data were also analysed by gener- alised Procrustes analysis (GPA; Oreskovich et al 1991) using Procrustes-PC v 2.2 (Oliemans, Punter and Partners). Time-intensity senlsory curves were analysed

2-tieptanone

I I, I0 I , Lo 7, a

Time (s)

Cpd-I0

by PCA (Dijksterhuis 1993) as a means of averaging across the panel without distorting the results or losing information on inter-assessor variability.

PCA and PLS was also used to model the relationship between the temporal instrumental and temporal sensory data, but as assessors sensory T-I data vary in relation to their rate of consumption both blocks of data were first aligned into a common format by time of swallow. The swallow-adjusted-time (Tadj) for each T-I assessor was calculated to represent the same proportion to time of swallowing (STA, see Table 3 below), as each buccal headspace time (Tact, 15, 30,45 or 60 s) was to swallow time during buccal headspace collection (ST,), ie

Tad, - T a c t

STA ST,

Thus, a sample of buccal headspace data taken one- quarter of the way through consumption was matched to T-I data taken one-quarter of the way through consumption, irrespective of the actual consumption times concerned. Secondly, since the temporal instrumental data were cumulative, the sensory T-I data were read as the maximum attribute intensity perceived by each assessor once this maximum was reached. Models were calculated on the four measured temporal buccal headspace times of 15, 30, 45 and 60 s of cheese consumption.

RESULTS

Standard buccal headspace

Twenty-four compounds were chosen from buccal headspace extracts of the eight cheeses analysed. These compounds were reproducibly present in the cheese extracts, and not present in the blanks. These cheese scores and the loadings of the 24 compounds, from the first two components of a PCA (which accounted for 32% and 18% of the variance respectively) on log transformed peak areas, are shown in Fig 1. The headspace data were log transformed because, over the range of concentration found, the simplest relationship between a perceived intensity and the stimulus magnitude is a log function. Thus the sensory intensities should be better correlated with the log of the compound amount or concentration than with the actual amount. PCA was carried out on duplicate data (ie the observation) and the means of the component scores of each pair of duplicates calculated and plotted. ANOVA showed significant differences between samples for both components ( P = 0.012 and P = 0.04 for principal

. I

component (PC) 1 and 2, respectively). The k% confidence from the pooled SD Of the component Scores is shown. C k ~ s e s 1, 2, 5 and 6 are full-fat cheeses, and 3,4, 7 and 8 are reduced-fat.

Fig 2. Release of volatile compounds from four cheeses with time during consumption. cl , cheese 1; c2, cheese 2; c3, cheese 3; c4, cheese 4. Pooled standard deviations for duplicates are

shown for each compound.

276

TABLE 1 Analysis of variance of temporal buccal headspace data

C M Delahunty et a1

1:16 1 : 40 1 : 49 1 : 53 1 : 59 2 : 12 2 : 33 2 : 45 3 : 09 3:16 3 : 33 3 : 42 4 : 05 4:lO 4 : 25 4 : 35 6 : 56 7:lO 7 : 14 8:18

13 : 06 13 : 46 16 : 14 17 : 16

Cpd-1 C p d Z Cpd-3 Ethyl acetate 2-Butanone Cpd-6 Cpd-7 2-Pentanone C p h 9 Cpd-10 2-Butanol Cpd-12 Cpd-13 Cpd-14 Cpd-15 Cpd-16 Cpd- 17 Cpd-18 Cpd-19 2-Heptanone Cpd-21 Cpd-22 Cpd-23 2-Nonanone

NS" NS NS NS

** NS NS NS NS NS NS NS

NS NS NS NS ** NS NS NS *** NS NS NS ** NS NS NS *** NS NS NS NS ** * NS

** NS NS NS

** NS NS NS NS NS NS NS NS * * NS * * ** NS

*** ** NS NS *** * * NS * NS NS NS

*** NS NS *** * NS NS ** * NS NS

**b *** * *

* *** * **

*** *** * **

** NS *** ***

***

NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS

a NS, not significant. *** P < 0.001; ** P < 0.01; * P < 0.05.

Temporal buccal headspace

Examples of release patterns during consumption for 12 out of the 24 compounds are shown in Fig 2, for four cheeses (cheeses 1-4). In all cases the cheeses were swallowed at 52 2 s. Pooled standard deviations for each compound for duplicate analyses are given. ANOVA showed significant effects of time period and of cheese for most compounds, of fat content for seven compounds, and a significant interaction between time and cheese for four of them (Table 1). There was no effect of interaction between time and fat content. Relative release rates of the methyl ketones identified are shown in Table 2. A PCA of the log transformed temporal data is shown in Fig 3. PC1 accounted for 42% of the variance and PC2 accounted for 19%. ANOVA showed significant differences between samples on both components (P=0.003 and P=O.OOO on PC1 and PC2, respectively). 2-Butanone, 2-butanol, and compounds 14, 16 and 22 had near-zero loadings on PC1 and compounds 7, 9, 10, 13, 23, 2-pentanone and 2-heptanone had large positive loadings, associated with later stages during cheese consumption. 2-Butanone, 2-butanol and compound 12 had high loadings on PC2, associated with cheese 3 and to a lesser extent cheese 4 (both

reduced-fat cheeses). Compounds 14, 16 and 22 had high negative loadings on PC2, associated particularly with the later stages of consumption of cheeses 1 and 2 (both full-fat).

Conventional descriptive sensory analysis

A PCA of the data is shown in Fig 4. The first two components accounted for 35% and 24% of the

U a 4 E 3

a 2

- 0 .d a -1

$ 1

: - 2 2 -3 .d

~

- 8 - 8 - 4 - 2 0 2 4 6 -0.6 -0.1 0.0 0.1 0.2 0.3 0.4


Fig 3. Principal component scores for four cheeses at four time intervals and loadings of compounds from PCA of temporal buccal headspace data. C115, volatiles released from cheese 1 during 15 s cheese consumption etc. The 95% confidence interval is shown, calculated from duplicate analyses.


TABLE 2 Relative release of methyl ketones isolated from Cheddar cheese by temporal buccal headspace. The figures represent a percentage of the

maximum compound peak area in each case

Time 2-Butanone 2-Pentanone 2-Heptanone 2-Nonanone

Cheese I (full-fat, 35%) l!j ND" 30 ND 45 ND 610 ND

Cheese 2 (full-fat, 35%) 1 :5 ND 30 ND 45 ND 680 ND

Cheese 3 (reduced-fat, 23%) 1 :5 7 30 20 45 35 60 100

Cheese 4 (low-fat, 16%) 1.5 ND 30 20 45 115 60 100

37 48 84

100

20 39 51

100

39 42 81

100

26 43 76

100

10 21 55

100

8 17 36

100

19 30 60

100

10 35 63

100

ND 33 59

100

16 18 43

100

44 69 65

100

ND 30 73

100

a ND, not detected.

variance, respectively. ANOVA showed significant differences between samples on both components (P = 0.002 and P = 0.001 on PC1 and PC2, respectively). PC2 differentiated cheeses by fat content, and PC1 within each fat content group. The positive pole, associated with full-fat cheeses, had high loadings for the descriptors cheesy, buttery, milky, nutty, salty and strength. The negative pole, associated with reduced-fat cheeses, had high loadings for the descriptors processed and sulphurous. The descriptors cheesy, milky, processed and rancid were identified as charac- teristic of the components. GPA confirmed that these

.z - 3 1%": , , , 1 --0.4 t , , ,*_,,,, 1

.* 2 - 4 -0.6 -4 - 2 0 2 4 -0.6-0.4-0.2 0.0 0.2 0.4


Fig 4. Principal component scores for four cheeses and loadings of descriptors from PCA of conventional descriptive sensory analysis data. The 95% #confidence interval is shown,

calculated from triplicate analyses.

descriptors were used consistently by the assessors, and ANOVA showed that they discriminated between samples (P < 0.05; for rancid, P < 0.10). Consequently, these were chosen for T-I assessment.

Time-intensity sensory assessment

First principal scores had second principal scores for a non-centred PCA of the four descriptors chosen, for the four cheeses (cheeses 1-4) are shown in Fig 5. The descriptors cheesy, milky (full-fat) and processed (reduced-fat) clearly distinguished cheeses according to fat content. The loadings on PC1 (77%) and PC2 (13%) of the nine assessors in duplicate for the descriptor milky for cheese 1 (Fig 5 ) are shown in Fig 6. This shows that, while there were differences between assessors, they were generally reproducible. Table 3 lists the mean swallow times for each assessor calculated from all samples. The 95% confidence intervals on replicate swallows are again indicative of assessor repro- ducibility. The upturn seen in curves for PC1 in Fig 5 between 30 s and 45 s was due to an increase in the intensity of flavour perceived immediately after swallowing. Figure 7 shows the sample scores from a PCA of the T-I data for the attribute 'processed' adjusted for swallow time as described above and averaged across assessors. The first two components accounted for 52% and 25% of the variance, respectively.

278

\

C M Delahunty et a1

Processed 6 0 O I , , . . . ,

Cheesy 300 7,

-300 0 15 30 45 60 75 90

100

0

-100

-200

-300 0 15 30 45 60 75 90

Processed

-300 0 15 30 45 80 75 80

Rancid ul

Rancid 600 ,-, 300 I

i 500 1

-300 .i 0 15 30 45 80 75 80

Time ( s ) Fig 5. First principal and second principal scores of non- centred PCA of time-intensity data for four flavour attributes

for four cheeses.

Relationships between sensory and instrumental data

Buccal headspace data were used as the X data set and conventional descriptive analysis data as the Y data set for PLS modelling of the relationships between the two sets. The predicted vs measured correlation coefficients,

* -0.8 -u.4 L 0.0 0 .2 0.4 0.6

F i r s t p r inc ipa l loadings

Fig 6. Loadings of assessors from PCA of time-intensity data for flavour attribute milky for cheese 1.

-5 -4-3-2-10 1 2 3 4

Pr inc ipa l c o m p o n e n t 1

Fig 7. Principal component scores for four cheeses a t four time intervals from PCA of swallow adjusted time-intensity sensory assessment of the flavour attribute 'processed'. C115, panel perception of attribute intensity for cheese 1 at IS s etc.

for the attributes assessed by T-I, using two PLS components (selected by cross-validation) were r = 0.75 (cheesy), r = 0.68 (milky), r = 0.68 (rancid) and r = 0.74 (processed).

For PLS of temporal data, the log transformed temporal buccal headspace data for 15, 30, 45 and 60 s of consumption were used as the X data set, and the swallow adjusted T-I attribute intensities for each assessor as the Y data set. Figure 8 shows the attribute intensity scores, calculated from the PLS model, plotted against the swallow-adjusted observed attribute intensities. One PLS2 model was calculated for each attribute. Good predictions were achieved for all four attributes on 2 PLS components, but it can be seen that cheesy and processed were best predicted. The predicted vs measured correlation coefficients for each assessor.

Cheesy 2 5 0 , . . . . , I

200 .

2 150 . .r(

m E loo .

Q) 4 50

.- 0 -

. d

* (d Rancid A

1

Milky 250 I

-50 -50 0 5 0 100 130 200 250

Processed 2 5 0 , , . , , .

- .. -50 0 50 100 150 200 250

Observed flavour intensity Fig 8. Partial least squares regression predictions from temporal buccal headspace data of 'cheesy', 'milky', 'rancid' and 'processed' flavour attributes, in four cheeses a t four time intervals for nine assessors. Correlation coefficients between observed and PLS predicted scores are shown for each asses-

sor.


for each descriptor, are shown (Fig 8). These values were generally considerably better than those obtained from the conventional data above, but for assessor H, r = 0.36 on 2 PLS components for processed. In fact, the first minimum in the residual validation variance occurred at three components, but the reduction between two and three components was very small and so a two-component model was selected to avoid fitting an over-optimistic model. Hfowever, for assessor H, r = 0.96 on three components whereas there was minimal improvement in fit for the other assessors. Assessor H therefore must have behaved slightly differ- ently, and required the third component for a satisfac- tory fit. Predictions of cheesy and milky showed high loadings for the compounds; associated with full-fat cheeses. Prediction of rancid and processed showed high loadings for the comlpounds associated with reduced-fat cheeses. Assessors who perceived an attribute rapidly had the highest loadings for the compounds released rapidly from the cheese. For example, assessor H perceived rancidity very rapidly, and had high loadings for 2-butanone and 2-butanol in the PLS model.

DISCUSSION

Standard buccal headspace (Fig 1) showed clear differences between the cheeses. Separation by fat content was not very clear, as cheese 5 drifted towards the reduced-fat cheeses. Conventional descriptive analysis also separated cheeses, and groups by fat content (Fig 4). The PCA loadings (Fig 4) show which flavour attributes were most distinguishing for a particular cheese. For example cheese 1 was particularly milky. Cheeses 3, 5 and 7, which were describedl as most rancid, had high loadings for 2-butanone and 2-butanol, which increase in concentration as a cheese matures (Urbach 1993). Excessive quantities may be I-esponsible for, or indicative of, off-flavour. Cheeses 1, 2 and 6, all full-fat, were described as cheesy and had high loadings for some methyl ketones (2-pentanorie, 2-heptanone and 2- nonanone). Large differences in ketone concentrations, related to fat content, have been reported previously (Banks 1993). The flavour attributes cheesy and processed received high scores for full-fat and reduced-fat cheeses, respectively. The PLS model predicted these flavour attributes from the buccal headspace data, and thus cheeses could be classified accordingly. Although the compounds isolated appear to be important for aspects of Cheddar flavour character, it is unlikely that they can characterise Cheddar flavour. It is most likely that combination with certain sulphur compounds (Delahunty et a1 1996) and non-volatiles (Urbach 1993) is required.

The purpose of temporal methodology was to allow us to examine relationships identified by conventional methods in more detail. The release behaviour of indi-

vidual compounds during cheese consumption, analysed by temporal buccal headspace methodology, is shown in Fig 2. ANOVA (Table 1) showed that increasing levels of most compounds were released during consumption, and that some compounds were released at different levels from different cheeses. In the case of 2- butanone and compounds 2, 12 and 14, the rate of release was dependent on the cheese (time x cheese interaction). Fat content also influenced release behaviour of some compounds, including 2-heptanone which was seen to be important for distinguishing full-fat from reduced-fat cheeses by conventional methods. No significant time x fat interaction was observed, and so there must have been other influences, eg effects of protein and salt concentration (Kinsella 1990), causing the time x cheese interaction. The relative release rates of the series of methyl ketones identified are shown in Table 2. It would be expected that the release rate would slow as carbon chain length increased due to the increasing hydrophobicity in this homologous series. It would also be expected that this effect would be greatest in full-fat cheeses (Buttery et a1 1973). Thus, in Table 2, the proportion of maximum amount released at 45 s would have been high for short-chain ketones and from cheeses of lower fat content (indicating early release from the cheese). In contrast, proportion of maximum would have been low for long-chain ketones and from cheeses of higher fat content (indicating later release). The data appeared to show a trend towards this effect, but the results were complicated by large differences in the quantities of individual compounds present, eg between 2-butanone and 2-nonanone. Again other influences on the compounds’ behaviour must be considered. The amounts of 2-butanone and 2-heptanone released were influenced by fat content (Table l), and the rate of release was higher in the reduced fat cheeses (Table 2), shown by the greater proportions released at all stages up to 60 s.

PCA of the temporal instrumental data (Fig 3) provided results in agreement with, and confirming, the standard buccal headspace data (Fig 1). Cheese 3 was found to be considerably different from cheeses 1, 2 and 4 in both data sets. The temporal data allowed these differences to be followed at 15 s intervals during the consumption. Three major differences in the results for cheese 4, the lowest in fat content (16%), could be identified. Firstly, it released a smaller total amount of volatiles (C460 had the lowest score on component 1 of all the 60 s observations). Secondly, the change with time on PC2 differed from the other cheeses, and thirdly there was a more rapid release of compounds between 15 and 30 s. Although initial compound levels were low, the release rate during consumption was rapid. This differing release behaviour may be caused by the cheese’s reduced-fat content.

The perception of individual flavour attributes with time (Fig 5) agreed with the conventional descriptive

280 C M Delahunty et a1

sensory results. From the first principal scores of a non- centred PCA on T-I data, the rate of onset, the maximum intensity, the rate of decline and the persist- ence of the individual flavour attributes can be seen. The second principal scores appear to follow the rates of rise and decline of the first principal scores (Dijksterhuis 1993). For example, cheeses 1 and 2 were more cheesy in all respects. From the second principal scores, the full-fat cheeses also showed a more rapid rate of onset of this attribute. This counters the sugges- tion that reducing fat content will hasten flavour perception (Gurr and Walstra 1989). The influence of a greater quantity of cheesy flavour inducing compounds in full-fat cheeses must have countered a more rapid rate of release in the reduced-fat cheeses. The cheeses were also separated by fat content for the attributes milky and processed. This was expected from the conventional sensory results. The relative intensities of each individual attribute, and their changing balance during consumption, will affect the overall flavour experience. This effect has been demonstrated previously for the attributes cheesy and fruity, where fruity was perceived sooner and more intensely in low-fat cheeses because of decreased cheesiness (Delahunty et a1 1996). In this case the differences in individual attribute perception were not as striking, but small time differences could have a large effect on overall perception as olfaction processes are very rapid (Laing 1994). Cheese 3 showed a very fast onset of rancidity. ,This agreed with the temporal instrumental results, where the PCA loadings plot shows 2- butanone and 2-butanol as important compounds early in the consumption of cheese 3.

The assessors varied in how they rated the cheeses (Fig 6), although it can be seen that they reproduced their own results. Differences in individual perception, and simply in scaling equal stimuli, have been widely documented (Dijksterhuis 1993). Swallow times (Table 3) also varied considerably between assessors and very little within an assessor's data. The assessors with high loadings on the y-axis (Fig 6), eg b, c, g and h, were

TABLE 3 Mean swallow times for individual assessors based on 32 cheeses (4 cheeses x 4 descriptors, in duplicate) sampled for

T-I analysis

Assessor Mean swallow time 95% CI

46.9 1 33.66 27.44 47.59 33.59 43.9 1 1841 27.97 67.44

44.12-49.69 31.58-35.73 25.33-29.54 44.38-50.81 31 *41-35.77 40.79-47.02 17.67-19.95 26.78-29.16 64.04-70.83

those who had the fastest rising and declining curves. High loadings on the x-axis, eg b and c, indicate those with the curves which best fitted the first principal scores.

Comparison of Figs 1 and 4 showed some similarities in the patterns of the cheeses, and PLS modelling of the relationships between the standard buccal headspace and conventional sensory data showed some predictive ability. It was possible to predict some of the cheeses' flavour character and to predict the fat content of a cheese on three attributes, cheesy, buttery and processed. However, consideration of time-courses of flavour release and perception allowed the flavours to be examined in more detail. PCA of the swallow adjusted and reduced T-I data (Fig 7) allowed the overall trend of perception for an individual attribute to be viewed in a simple and rapid way. The figure provided shows a PCA of the panel's perception of processed. Cheese 3 was very different from the other 3 cheeses, as was seen in the PCA of the temporal instrumental data (Fig 3). The onset (15 s and 30 s scores) of perception of processed for the two reduced fat cheeses (cheeses 3 and 4) was faster than for the full-fat cheeses. Dividing instrumental and sensory data into four time intervals reflecting release behaviour during consumption, and using this data for PLS regression, improved the model considerably (Fig 8). It is clear that the dynamics of volatile release from cheese must be considered to ade- quately describe flavour perception.

CONCLUSIONS

Conventional descriptive sensory analysis clearly discriminated between hard cheeses and between cheeses of differing fat-content. Flavour attributes associated with full- and reduced-fat cheeses were identified. Buccal headspace analysis also discriminated between cheeses. These compositional data predicted some flavour attributes including two, cheesy and processed, which separated cheeses by fat content. Temporal data sup- ported and amplified the conventional results. Com- pound release behaviour differences between cheeses, important for flavour perception, were found. PCA of the standardised T-I sensory data provided a simple and rapid means of viewing cheeses' overall flavour release differences with time. The temporal data provided improved sensory predictions from the instrumental results. The dynamics of volatile release from cheese are an essential determinant of flavour.

ACKNOWLEDGEMENTS

The authors wish to thank the Commission of the European Union and the UK Biotechnology and Bio-

Flavour release behaviour from hard cheeses 28 1

logical Sciences Research Council (formerly the Agricul- tural and Food Research Council) and Chivas Brothers Ltd for financial support.

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Documents

Comparison of Dynamic Flavour Release from Hard Cheeses and Analysis of Headspace Volatiles from the Mouth with Flavour Perception during Consumption