Development of a model for intensity of flavourcharacter in blackcurrant concentratesRaymond K Boccorh, Alistair Paterson* and John R PiggottCentre for Food Quality, University of Strathclyde, Department of Bioscience and Biotechnology, 204 George Street, Glasgow G1 1XW,Scotland, UK

Abstract: The feasibility of routine quanti®cation of ¯avour character intensity in fruit concentrates by

chromatography was studied. A set of 37 volatile components, previously identi®ed as ¯avour-active by

gas chromatography-olfactometry, were quanti®ed in 133 different blackcurrant concentrates from

three seasons, by gas chromatography with ¯ame ionisation detection. Sensory data on intensities of

¯avour character in these concentrates were obtained using ratio scaling. Partial least squares

regression (PLS) was employed to develop models describing relationships between intensity of ¯avour

character and concentrations of speci®c ¯avour components. Separate models were obtained for

concentrates produced within three single seasons. Individual concentrates varied in: geographical

origin; post-harvest fruit storage (chilled or frozen); and concentration strategy, either conventional

thermal or freeze technology. Cross-validation (by PLS) determined the number of correlation factors

required to reach minimum prediction error for each model. The ®nal model had a regression

coef®cient of 0.80, dependent upon 10 ¯avour components, and was suited for predicting ¯avour

character intensity in blackcurrant drinks from concentrates.

# 1999 Society of Chemical Industry

Keywords: partial least square regression; multivariate statistical modelling; fruit ¯avour; fruit drinks; gaschromatography-olfactometry; sensory compositional relationships

INTRODUCTIONIn fruit drinks industries, it is important that products

are of consistent ¯avour quality and intensity despite

variations in availability of the fruit concentrates which

are primary ingredients. Although concentrates can be

blended to minimise sensory differences, manufac-

turers cannot currently routinely determine level of

concentrate required to obtain a predetermined

intensity of ¯avour character by a compositional

analysis.

Multivariate statistics can facilitate understanding of

relationships between ¯avour composition and sensory

character,1 correlating sensory factors, such as attri-

bute intensity, with chromatographic data. Williams

and his coworkers2,3 have summarised the different

options. Whereas least squares curve ®tting, and other

classical correlation approaches, model important

sources of variance, these may ignore other data

yielding important information on overall ¯avour

character and be suitable only for model systems.1 In

contrast, inverse calibration methods (eg multiple

linear regression, canonical correlation analysis and

redundancy analysis) compensate for all, including

minor, sources of variance on the basis that every

variable contains unique information. Theoretical

considerations suggest neither is well suited to

chromatographic data, failing to satisfy key criteria

for identi®cation of meaningful relationships.2,3

Further, for both approaches, the number of samples

should exceed that of the total of variables which is not

always possible in practice.

Two intermediate methodologies, principal compo-

nent (PCR) and partial least squares (PLS) regression

have been used to model primary sources of variance

in ¯avour data. PCR summarises and extracts the

maximum of information, via principal component

analysis, in the original variables but in practice may

yield models with poor predictive abilities. One

problem is that PCR shows a bias towards variables

accounting for large proportions of variance indepen-

dent of sensory signi®cance. However, PLS both

summarises information in variables and maximises

explanation of speci®c sensory variables, minimising

prediction error. In summary, PLS may not yield

models with the predictive ability of multiple linear

regression or redundancy analysis, or the ability to

summarise analytical data of principal component

regression, but does provides robust models that

minimise prediction errors.

The aim was to clarify the compositional basis of

overall ¯avour character intensity in thermal evapora-

tive fruit concentrates, speci®cally from blackcurrants.

Journal of the Science of Food and Agriculture J Sci Food Agric 79:1495±1502 (1999)

* Correspondence to: Alistair Paterson, Centre for Food Quality, University of Strathclyde, Department of Bioscience and Biotechnology, 204George Street, Glasgow G1 1XW, UKE-mail: cdas01@strath.ac.uk(Received 16 March 1998; revised version received 1 April 1999; accepted 20 April 1999)

# 1999 Society of Chemical Industry. J Sci Food Agric 0022±5142/99/$17.50 1495

It was also important to determine if a suf®ciently

robust model could be developed to be used with

concentrates of different geographical origins, seasons

and prepared from fruits stored at either�4 orÿ20°C.

Earlier studies4,5 had identi®ed by gas chromatogra-

phy/olfactometry (GCO), 37 volatile components in

concentrates which were ¯avour-active (Table 1). PLS

was chosen to model linear relationships between

concentrations of such components, from GC data,

and a single sensory factor, intensity of ¯avour

character (PLS 1), obtained by ratio scaling. A

previous study6 developed a model for a restricted

set of blackcurrant concentrates using total GC data

from a ¯ame ionisation detector. In this study a wider

range of concentrates was studied and compositional

data edited to include only the ¯avour-active compo-

nents.

MATERIALS AND METHODSQuantification of flavour componentsBlackcurrant concentrates used were described pre-

viously.5 Flavour components were concentrated by

sorbent extraction and quanti®ed as described else-

where.7 Integrated GC data on ¯avour-active compo-

nents were normalised to unitary variance. For each

sample duplicate extractions were made, and for each

extract duplicate injections were performed and mean

values used for construction of data sets. Flavour

components were discriminated into character-enhan-

cing or character-diminishing on the basis of char-

acterisation of extracts obtained using different

sorbent eluents and gas chromatography/olfactome-

try.4,8

Determination of flavour character intensity inconcentratesRatio scaling of ¯avour intensity in model blackcurrant

drinks was carried out as described elsewhere.9 Prior

to statistical analysis, individual assessor scores for

samples were pooled to two standards by modulus

normalisation.10 This brought sample scores within a

single consensus range, eliminating variation between

assessors. Sensory analyses were performed in dupli-

cate sessions employing balanced presentations.11

Statistical modellingModelling of relationships between data sets was

effected using PLS1 relating compositional to sensory

data, employing Unscrambler, v3.55 (CAMO A/S,

N-7041 Trondheim, Norway).

RESULTSConcentrates from 1989 fruitFor 1989 concentrates, cross-validation showed two

signi®cant PLS factors explaining 58% variance (Fig

1) Discriminating on the basis of chilled or frozen

storage of fruits on the ®rst Factor, separated New

Zealand and fresh fruit concentrates. Factor 2

separated New Zealand and frozen fruit from fresh

UK berry concentrates. From PLS loadings, 17

¯avour components in¯uenced this model. Variables

conferring character-enhancing cooked beans and spicyaroma notes were best described by Factor 1, with

positive loadings and highly intercorrelated. Also

described by Factor I were components with dimin-

ishing faecal, burnt beans and dried hay aroma notes,

with negative loadings. These were negatively corre-

lated with character-enhancing components. On

Factor 2 components with enhancing cooked beansand burnt incense notes had high positive loadings, and

those with character-diminishing notes had negative

loadings. A single compound with the enhancing

manure-like note was clustered with components with

diminishing notes. Clustering of variables was pro-

nounced particularly with negatively loaded wilted¯ower, dried hay and burnt rubber notes. The compo-

nents with a leathery note had no in¯uence in the

model.

Figure 2 shows the PLS plot of predicted against

Table 1. Aroma-enhancing and diminishing notes with majorinfluences on the character of blackcurrant concentrates

Aroma-enhancing notes Aroma-diminishing notes

Cooked beans (Cb) [3] Faecal (Fc) [1]

Spicy (Sp) [3] Wilted ¯ower (Wf) [1]

Manure-like (Ma) [3] Burnt beans (Bb) [5]

Burnt incense (Bi) [5] Burnt rubber (Br) [5]

Leathery (Lea) [1] Tree bark (Tb) [1]

Old leather-like (Ol) [4] Dried hay (Dh) [2]

Smoky (Sm) [3]

Abbreviations of aroma notes on biplots are shown in parenth-

eses. Figures in right square brackets are the total number of

GCO peaks with that note.

Figure 1. Biplot of first and second PLS Factor scores for (*) chilled and(*) frozen fruit UK, and (!) New Zealand 1989 concentrates and loadingsof flavour components.

1496 J Sci Food Agric 79:1495±1502 (1999)

RK Boccorh, A Paterson, JR Piggott

measured ¯avour character intensity. A correlation

coef®cient of 0.39, indicated low predictive ability.

Concentrates from New Zealand and frozen UK fruits

were outliers and contributed to large errors. There

was no differentiation of concentrates on the basis of

differences in post-harvest storage of fruits.

Concentrates from 1989 UK fruitFigure 3 shows the perceptual space representing 1989

concentrates after removal of certain outliers, concen-

trates from New Zealand and two UK fresh fruit (Fig

1). Cross-validation showed 39% of variance was

explained in the ®rst two Factors which were

signi®cant. Use of chilled or frozen fruit in concentrate

production was not important in differentiating scores

of concentrates on these factors. There was also no

clear separation of the 26 ¯avour components on the

basis of either enhancement or diminution of ¯avour

character. Character-diminishing components with

burnt rubber and burnt beans notes were, however,

positively loaded on Factor 1 and a pair of highly

correlated compounds with character-enhancing man-ure-like and burnt incense notes with lower scores were

obvious. Compounds with strong negative loadings on

this Factor were mostly character-enhancing compo-

nents with burnt incense, cooked beans, spicy, old leather-like, and manure-like notes. There was marked

clustering of components with similar aroma char-

acters, notably with character-diminishing burnt beansand faecal notes. Highly loaded both positively and

negatively on Factor 2 were compounds with both

enhancing and diminishing notes, notably burnt incenseand burnt rubber. Two components enhancing char-

acter were clustered with compounds with character-

diminishing notes, all with negative scores. The plot of

predicted versus measured ¯avour intensity (Fig 4)

had a high correlation coef®cient, 0.74, through

removal of outliers. This model also suggested con-

centrates from chilled fruit had lower character

intensity than those from frozen.

Concentrates from 1990 fruitCross-validation suggested the ®rst three PLS compo-

nents (60% variance) were signi®cant. The ®rst two

factors (Fig 5) explained 46% variance. Discriminated

on Factor 1 were two concentrates, one from frozen

UK fruit, and a freeze concentrate. Factor 2 suggested

four Imported concentrates and a single Polish

Figure 2. Measured and predicted flavour intensity scores from chilled andfrozen fruit UK and New Zealand 1989 concentrates (symbols as in Fig 1).

Figure 3. Biplot of first and second PLS Factor scores for chilled and frozenfruit UK, and loadings of flavour components (symbols as in Fig 1).

Figure 4. Measured and predicted flavour intensity scores from chilled andfrozen fruit UK 1989 concentrates (symbols as in Fig 1).

J Sci Food Agric 79:1495±1502 (1999) 1497

Model for ¯avour character in blackcurrant concentrates

concentrate were outliers. Factor 3, 14% of variance,

differentiated some UK fruit from foreign fruit

concentrates.

On the positive side of Factor 1 was a single com-

ponent with a character-diminishing dried hay note

important in frozen UK fruits and freeze concentrates.

Again components with similar notes showed cluster-

ing, but not through contribution, or diminution of

character but this was observed on Factor 2 where

enhancing components with negative loadings, had

burnt incense and spicy notes. These were strongly

correlated with four concentrates from Imported fruit.

With positive loadings on Factor 2 were components

with diminishing burnt rubber, burnt beans and dried haynotes, related to frozen UK fruit or associated with

freeze concentrates; notable were two components

with old leather-like notes.

Plotting predicted against measured character in-

tensity, employing the three signi®cant factors (Fig 6)

yielded a correlation coef®cient of 0.61, and concen-

trates from chilled fruit were not well modelled.

Recalculation of data from UK, Polish and New

Zealand fruit, omitting outlier Imported fruit and

freeze concentrates, yielded three signi®cant factors

(45% variance). Factor 1 (Fig 7) discriminated certain

concentrates from chilled UK and Polish fruit. Factor

2 separated concentrates from Polish and New

Zealand fruit and certain UK concentrates. Factor 3

differentiated the two concentrates from New Zealand

fruit, indicating a leverage effect. Positively loaded on

Factor 1 were a number of components conferring

enhancing old leather-like and spicy aroma notes,

abundant in fresh UK fruit concentrates. Components

with old-leather-like notes were well correlated with

positive loadings. In contrast, with negative loadings

were components with manure-like and cooked beansnotes enhancing character. An inverse relationship was

observed between such components and use of chilled

fruit. Factor 2 discriminated components diminishing

character. Positively loaded were components with

burnt rubber, tree bark, wilted ¯ower and dried hay notes,

also abundant in concentrates from Polish fruit.

Components with faecal, and smoky aroma notes

played no major roles in the 1990 model.

The model for prediction (Fig 8) had a correlation

coef®cient of 0.59: thus removal of data for Imported

concentrates had reduced predictive ability.

When only data for frozen 1990 UK fruit, and freeze

and foreign concentrates, were considered, cross-

Figure 5. Biplot of first and second PLS Factors: chilled and frozen fruit UK,(^) freeze, (&) Imported, (~) Polish and New Zealand concentrates andloadings of flavour components (symbols as in Fig 1).

Figure 6. Measured and predicted flavour intensity for 1990 concentrates.

Figure 7. Biplot for the first and second PLS Factors: chilled and frozen fruitUK, Polish and New Zealand 1990 concentrates (symbols as in Figs 1 and5) and loadings of flavour components.

1498 J Sci Food Agric 79:1495±1502 (1999)

RK Boccorh, A Paterson, JR Piggott

validation suggested the ®rst two factors (34%

variance) were signi®cant (Fig 9). Factor 1 discrimi-

nated all concentrates from Imported fruit, two from

Polish fruit, the two freeze concentrates and a single

frozen UK fruit concentrate. These concentrates were

thus important for predicting intensity of ¯avour

character. Factor 2 has shown no relationship to

known differences: two freeze concentrates and a

single concentrate from Imported fruit were outliers.

Positively loaded on Factor I were components with

enhancing manure-like, smoky, burnt incense and spicynotes. Negatively loaded were diminishing burnt rubberand tree bark components. Such components were

abundant in conventional thermal concentrates from

Imported, UK and Polish fruit. Factor 2 separated

diminishing, from enhancing, components with posi-

tive and negative loadings, respectively: the former

abundant in Polish, Imported fruit, and freeze

concentrates. Imported fruit concentrates had higher

contents of components with aroma notes enhancing

and lower of diminishing, characters: the opposite was

the case with Polish fruit concentrates. The regression

plot (Fig 10), had a correlation coef®cient of 0.65.

Concentrates from 1992 fruitData from concentrates from 1992 fruit produced only

a single signi®cant PLS factor (14% variance). This

indicated a clear in¯uence of post-harvest fruit storage:

chilled and frozen fruit had respectively positive and

negative scores (Fig 11). Components enhancing

character were clearly discriminated with positive

loadings. A distinct cluster with negative loadings

included components with both enhancing and

diminishing notes, the latter dominant. Post-harvest

storage of fruit was important, with contributions from

components with aroma notes enhancing and dimin-

ishing character.

The regression plot (Fig 12) had a correlation

coef®cient of 0.70 with chilled fruit concentrates

having a higher character intensity than frozen.

Flavour intensity in concentrates from all threeseasonsA subset of 35 concentrates produced from 1989,

1990 and 1992 blackcurrant crops were selected on

the basis of differences in fruit post-harvest storage

Figure 8. Measured and predicted flavour intensity from chilled and frozenfruit UK, Polish and New Zealand 1990 concentrates.

Figure 9. Biplot for first and second PLS Factors: frozen fruit UK, freezeand foreign 1990 concentrates and loadings of flavour components.

Figure 10. Measured and predicted flavour intensity for frozen UK fruit,freeze and foreign 1990 concentrates.

J Sci Food Agric 79:1495±1502 (1999) 1499

Model for ¯avour character in blackcurrant concentrates

(chilled or frozen), geographical origin and processing

method. Cross-validation suggested a single signi®-

cant factor, explaining 15% variance. The perceptual

space (Fig 13) showed differentiation on Factor 1 of

freeze concentrates, and those from 1990 frozen fruit,

from those from 1989 fruit, New Zealand, and most

concentrates from chilled 1990 and 1992, and

Imported fruit. Concentrates from Imported fruit

were clustered with high positive loadings.

Positively loaded on Factor 1 were components

enhancing character: cooked beans, spicy, manure-like

and old leather-like. Also present were two components

which diminished character with burnt rubber notes.

Variables with negative loadings predominantly had

diminishing notes: dried hay, burnt beans, wilted ¯ower,burnt rubber and tree bark. However, this model (Fig

14) had a correlation coef®cient of 0.80. Notable was a

high contribution to ¯avour character of concentrates

from Imported fruit.

Figure 11. Biplot for first and second Factors: (*) chilled and (*) frozenfruit UK 1992 concentrates and loadings of flavour components.

Figure 12. Measured and predicted flavour intensity from chilled and frozenfruit UK 1992 concentrates.

Figure 13. Biplot on first and second PLS Factors for selected thermalconcentrates from: (*) chilled and (*) frozen UK fruit in 1989; (~) chilledand (~) frozen UK, (&) Imported and (!) New Zealand fruit in 1990;(^) chilled and (^) frozen fruit in 1992. Freeze concentrates prepared in1990 are shown as (!); abbreviations are loadings of flavour components.

Figure 14. Measured and predicted flavour intensity from randomlyselected concentrates from all sources and three seasons (symbols as Fig13).

1500 J Sci Food Agric 79:1495±1502 (1999)

RK Boccorh, A Paterson, JR Piggott

Prediction of flavour character intensity inconcentratesRegression coef®cients for all ¯avour-active compo-

nents in the ®nal model for the subset of concentrates

from all three seasons and fruit of differing geographic

origins showed a subset of 20 were important.

Leverages and residual variances, however, suggested

that 10 components, with both enhancing and

diminishing notes, had major in¯uences. Components

enhancing character included those with spicy,manure-like 1, cooked beans 1 and 2, old leather-like 2

and smoky 2 notes. Components diminishing character

included certain with wilted ¯ower, burnt rubber 1 and

dried hay 1 & 2 notes (Table 2).

From calculated coef®cients, intensity of ¯avour in

the selected concentrates could be predicted from the

following equation:

Y � 0:9033� 0:125�SP2� � 0:54�MAN1�ÿ 0:61�SM2� ÿ 0:026�CB1� � 0:034�CB2�ÿ 0:135�OL2� ÿ 0:151�WF� ÿ 0:003�BR1�ÿ 0:042�DH1� � 6:26�DH2�

DISCUSSIONDrinks manufacturers could predict intensity of

¯avour character from compositional data, but there

are few reports.6,12±14 The PLS 1 model for ¯avour

intensity in diverse concentrates of three seasons was

signi®cantly better than for individual years. Dis-

cussions on enhancement or reduction in ¯avour

character were based upon sensory studies of sorbent

and liquid/liquid extracts from a single blackcurrant

concentrate7,8 and originate in many products having

unpleasant aroma notes.15±17 Processed fruit ¯avour

components have similar origins in thermal modi®ca-

tions of labile ¯avour compounds and their precur-

sors.18

The 1989 concentrates model included only those

from a single production site following elimination of

outliers (New Zealand and two UK concentrates from

fresh fruits) enhancing predictive ability. Other con-

centrates showed marked variations in contents of

components both enhancing and diminishing ¯avour

character. The relevance of either enhancing or

diminishing overall character intensity merits consid-

eration. Among 1990 concentrates there was no clear

separation on this basis on the ®rst PLS Factor, and

this was not related to prediction, whereas in the 1992

concentrates model there was a clear relationship on

the ®rst Factor. This could be associated with the

heterogeneous composition (varied geographical ori-

gin) of 1990 concentrates. A group of similar

concentrates from a single season7 should yield a

model with enhanced predictive ability. However, the

reverse was observed in that the ®nal model, for a

randomly selected subset of concentrates, had greatest

predictive ability. Models for individual seasons could

be improved by elimination of outliers; ie those not

well modelled and away from the regression line. It is

however crucial to avoid elimination of extreme end-

member objects central to delineation of product

spaces.19 All outliers deleted in this study belonged to

the former group. Elimination of outliers may achieve

some improvement but at the expense of robustness.

The ®nal model coped with the characteristics of most

concentrates.

Sensory/compositional models can be discriminated

into the ad-hoc predictive and causative,20±22 this

model is predictive. Prediction of overall ¯avour

character remains technically challenging and merits

further theoretical and experimental studies.

Arti®cial neural networks form an alternative pre-

dictive strategy, exhibiting brain characteristics of

learning, adaptation and self-organisation,23 optimis-

ing establishment of both inter-and intra-relation-

ships.24 However, such prediction is not amenable to

logical analyses.25 Thus although neural networks may

be attractive for prediction of food quality, accept-

ability and preference in fruit drinks, there may be

problems when novel features are encountered.2,26

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