- Email: [email protected]
Descriptive analysis and external preference mapping of powdered chocolate milk
Food Quli~ 0
ELSEVIER
PII:
and Prcfcmce Vol. 9, No. 4, pp. 197-204, 1998 1998 Elsevier Science Ltd. A11 rights resewed Printed in Great Britain 0950.3293/98819.00+0.00
50950-3293(97)00064-5
DESCRIPTIVEANALYSISAND EXTERNALPREFERENCE MAPPINGOFPOWDEREDCHOCOLATEMILK Guillermo Hough* & Ricardo Sinchez Instituto Superior Experimental de Tecnologia Alimentaria, 6500 Nueve de Julio, Buenos Aires, Argentina (Accepted 24 October 1997)
descriptive analysis of chocolate milk but their focus was on the fat content of the milk used to prepare it. Stoer and Lawless (1993) used commercial chocolate milks to compare single product scaling and relative-to-reference scaling. &riven and Petty (1990) performed a consumer study with three commercial chocolate milks to test a
ABSTRACT Appearance, texture andjavor descriptors were developedfor powdered chocolate milk. The inruence of cocoa and gum concentrations on these descriptors was studied using stepwise multiple regression. Out of a total of 23 descriptors, four were non-signa&ant. For the sign;J;cant descriptors, the percentage variance explained ranged from 65 to 93%) with an average of 82%. Visual viscosityand oral thickness were correlated with instrumental viscosity. Principal component analysis showed appearance/texture was explained by four principal components, whilst aroma flavor was one-dimensional and depended on cocoa concentration alone. The circular ideal point model was chosen to map consumers on the first two appearance/texture principal components. For aroma flavor, consumers were mapped on a single dimension using an ideal point model. For both appearance/texture and aroma flavor, adults and children had similar averages but the ideal points for children were more spread out on the sample map. 0 1998 Elsevier Science Ltd. All rights reserved
discriminant function. Bodyfelt et al. (1988) mention desirable sensory characteristics and defects to look out for in chocolate milk. There are no reported studies on the influence of formulation ingredients on the sensory profile of chocolate milks. Hough et al. (1997) discussed possible formula variations in PCM. Milk solids were not varied to respect food regulations. Ideal level of sugar was measured and not varied in the response surface methodology experiment to avoid a bulkier product and because consumers could add more sugar if they wished. The two ingredients which were studied in more detail were gum and cocoa concentrations [(gum) and (cocoa), respectively]. External preference mapping is a tool which allows consumer data to be mapped on a multidimensional space, derived from other non-preference (external) data relating to the stimuli. The external space is usually obtained by principal component analysis of descriptive sensory data generated by a trained panel (Greenhoff and MacFie, 1994). External preference mapping is used in market research, yet, due to proprietary reasons, relatively few studies are reported in the literature. Reports
INTRODUCTION
are for: cat food (Jones et al., 1989), cheese (McEwan et chocolate confectionery (McEwan and al. ) 1989b), Thomson, 1989)) household fragrance (Nute et al., 1988)) lamb sausages (Helgesen et al., 1997), meat casserole (Greenhoff and MacFie, 1994), meat patties (Beilken et al., 1991)) olive oil (Pagliarini et at., 1994)) sweeteners (Tunaley et al., 1988) and tomato soups (Shepherd et at., 1988). There are no preference mapping studies for
The most common modes of consumption of chocolate milk are adding a cocoa powder formulation to milk or buying the chocolate milk ready to drink. Hough et al. (1997) optimized the formulation of a powdered chocolate milk (PCM) that only needs water for reconstitution. Sensory descriptive
analysis
of milk
(Claassen
chocolate milk. The objectives
and
Lawless, 1992; Raats and Shepherd, 1992) and milk chocolate (McEwan et al., 1989a; Aguilar et al., 1994) has been reported. Raats and Shepherd (1992) also performed *To whom 31 7-31309; Research Cientifiras,
(a)
to develop
of the present work were: the descriptors
and methodology
for
the sensory profile of PCM, (b) to establish the influence of gum and cocoa varia-
correspondence should be addressed. Fax: + +54e-mail: [email protected]. Both authors are Fellows of the Comision de Investigaciones Buenos Aires, Argentina
(c)
197
tions on the sensory profile, and to relate the sensory profile to consumer ability using external preference mapping.
accept-
198
G. Hough, R. Srinchez
MATERIALSAND
METHODS
Profile development To develop the profile, four samples were used: An initial formula (IF) chosen among formulations provided by industry and found in literature; composition is in Table 1. Details for reconstitution are reported elsewhere (Hough et al., 1997). Powdered cocoa mixed with milk according to packet instructions (Nesquik, Nestle Argentina S.A., Buenos Aires, Argentina). The two highest selling brands in Argentina of UHT chocolate milks ready to drink: Cindor (Mastellone Hnos. S.A., Buenos Aires, Argentina) and Sancor (Sancor Coop. Ltda., Sunchales, Argentina). These four samples were chosen because the study was on variations of chocolate and gum concentrations of IF,thus necessary descriptors would correspond to this sample. The other three were chosen to broaden the range of descriptors should other notes appear during formula variations. The panel consisted of 12 assessors trained in descriptive analysis of a number of products. Assessors received 40 ml of each sample in 100 ml glasses at room temperature, under artificial daylight illumination, and in individual booths. They were instructed to rinse with apple juice between samples. The list of descriptors and definitions were obtained by consensus, i.e. round table discussion of descriptors produced by each assessor. For flavor, standards of suggested descriptors were presented by the panel leader to help panel uniformity. To test the panel on the developed descriptors, the four samples were rated using 1Ocm unstructured lines anchored at the extremes. The samples were presented as before, numbered with 3 digit codes, balancing the order of presentation among assessors. Measurements were done in duplicate on separate days.
Formula variations Once the sensory profile had been developed, the effects of (gum) and (cocoa) on the sensory profile were studied. TABLE 1. Initial Powdered and Reconstituted Chocolate Formulation (corresponds to sample 1 of Fig. 2)
component Non-fat dairy solids Dairy fat Cocoa Sucrose Glucose Xanthan gum Locust bean gum Water
Milk
Powdered (% w/w)
Reconstituted (% w/w)
47.24 9.24 8.70 29.00 3.54 0.116 0.116 2.05
8.14 1.59 1.50 5.00 0.61 0.02 0.02 83.12
In a previous paper the convenience of varying (gum) and (cocoa) geometrically was discussed (Hough et al., 1997), obtaining the formulations shown in Fig. 1, which respond to a P-factor central composite design (Gacula, 1993). The resulting nine samples were analyzed in four sessions. In each session the IF (sample 1 from Fig. 1) was evaluated together with two other samples chosen at random among the eight remaining. The three samples in each session were presented in a balanced order. Thus four repetitions for the central point were obtained.
Consumer tests Sensory acceptability of the nine samples (see Fig. 1) were measured by 60 consumers, divided in 30 11/12-year-old children and 30 18/22-year-old young adults. Samples were evaluated for overall acceptability using a g-point hedonic scale going from ‘super good’ to ‘super bad’ (Kroll, 1990). As with the sensory profile, four sessions were used to evaluate the nine samples.
Instrumental viscosity Apparent viscosity of the nine samples was measured with a Brookfield LVT rotary viscometer (Brookfield Engineering Laboratories Inc., Stoughton, MA), using spindle no. 1 at 60r.p.m. For sample 7 (Fig. 1) spindle no. 2 at 60r.p.m. had to be used. Measurements were at lo-12°C similar to sensory assessments. To avoid thixotropic effects the samples were thoroughly stirred just before measurements.
Statistical analysis The results from testing the panel on the descriptors were analyzed by generalized Procrustes analysis (GPA) followed by principal coordinate analysis (Arnold and Williams, 1986) using a Genstat 5 (1993) procedure. The variation among individual assessments is replaced by a consensus configuration of samples, and assessors are tested for their closeness to the consensus. Assessors close to the consensus are deemed to perform well; those with large residuals perform poorly. Also analysis of variance (ANOVA) for each individual assessor was performed to monitor discrimination ability. Stepwise multiple regression was performed on each descriptor and on instrumental viscosity as a function of formula variations. The criterium to include a term was its p-value < 0.10. Principal component (PC) analysis addresses the correlations among the descriptors and exploits duplication of information to provide informative low-dimensional plots of the data to complement regression analysis. Also principal component scores were used for external preference mapping. For external preference mapping (EPM) the acceptance data for each individual were regressed against the
Analysis and Preference Mapping of Powdered Chocolate Milk
199
3
3
2 I
(0.75)
-1 -
8/ -2-
$
(0.375)
(0.016) I
(0.00s) I
-3-r -3
-2
-1
(O.& +--
(0.1)
0
1
(0.25) I
7
cI
2
L
Gum FIG. 1. Coded gum and cocoa concentrations (uncoded percentage concentrations in reconstituted as design variables in studying their effect on the sensory profile of powdered chocolate milk.
PC scores original
obtained
form
analysis
(Greenhoff
vector
model
one
from
the EPM
or
and
which
more
decreasing
the descriptive
algorithm MacFie,
implies
characteristic
is present,
if it is a negative
characteristic,
the characteristic
is present
point
model
ulus space which
implies there
from
ideal
the ideal
regardless instead
point
of circular do not
For Phase
the dimensions. were
taken
(1981)
point
a
that
Carroll
III
would
(1972)
IV EPM
between
calculations
Schiffman
et al.
5 (1993).
RESULTS
descriptors
are listed low-nil
(1) Undissolved chocolate on surface (undi) Chocolate in suspension (susp) Precipitated chocolate (prec) White spots on surface (spot) Mottled surface (mott)
Aroma (4) Total (aroma) Milky (milk-ar) Chocolate (choc-ar) Other aromatics
Appearance after stirring (2) Froth Dark Gloss Reddish (red) Blueish
Flavor (5) Milky (milk-fl) Sweet Bitter Chocolate (choc-fl) Vanilla (van) Residual (resid)
Texture Visual viscosity (3): measured visually by pouring sample from one glass to another (vise) Thickness (6): degree of thickness perceived by manipulating sample in the mouth (thick) Roughness (7): degree of roughness perceived by manipulating sample in the mouth (rough)
GPA
and
in Table
definitions
2. The
to high-extreme.
showed
that
all assessors
had
sufficiently
small
residuals (range = 0.12-0.25) for appearance and texture. For aroma and flavor one of the 12 assessors had a high residual (0.43 vs. a range for the remaining assessors of
Profile development The
Appearance
contours
to the acceptance
and
in Genstat
is the
distance
of the stim-
axes are rotated and
used
TABLE 2. Descriptors used for Sensory Profiling of Chocolate Milk (order of evaluation is indicated in parenthesis)
be constant
II has elliptical equally
II, III
the stim-
Phase
are in parentheses)
the less The ideal
at a fixed
thus the dimensions
and programmed
more
of a characteristic,
the acceptance Phase
within
point.
such
I the contour
from
or
the
it is liked.
strength
contribute
Phase
increasing
like an off-flavor,
an ideal
ones,
IV is the
it is liked; alternatively
at some
model
of direction.
ulus space model.
with
point
of
between
Thus
the more
is an ideal
will equate
circular
that
are
space.
the more
Phase
relationship
which
the stimulus
In its
four phases
1994).
a direct
characteristics
across
analysis.
includes
product
developed
end anchors
by the panel
of the scales
were
0.24-0.35) nate
and
analysis.
stantially
from
was an outlier Her
individual
average.
on the principal loadings
differed
coordisub-
200
G. Hough, R. Sdnchez
Pastor et al. (1996) suggested accepting p-values < 0.30 when using ANOVA to monitor assessor’s discrimination on individual descriptors. The assessor who was an outlier in the aroma/flavor GPA analysis had p-values >0.30 on the following descriptors: total aroma, bitter, chocolate flavor, vanilla and residual. She was excused from participating in the descriptive analysis of formula variations. Of the remaining assessors who were not outliers in the aroma/flavor GPA analysis, seven had all pvalues < 0.30, two had p-values > 0.30 for one descriptor, and two had p-values >0.30 for two descriptors. These assessors were retained.
Formula variations Data averaged over assessors were used for the regression analysis on (gum) and (cocoa) variations; results are in Table 3. Analysis of variance of the regression was not significant (p > 0.10) for mottled surface, vanilla, blueish and other aromatics. Mean intensities of these last two descriptors for all samples were less than 5 on a O-100 sensory scale; they were present in samples used for profile development, but not in those used to study formula
variations. These descriptors were eliminated from the PC analysis and consequently from EPM. For the rest of the descriptors (Table 3) the percent variance explained (R*) was > 65% and the p-value for the regression ANOVA was ~0.004, thus showing an adequate fit of design variables to sensory response. For each descriptor, terms included in the model were as expected; e.g. undissolved chocolate on surface is correlated negatively with (gum) and positively with (cocoa); bitter taste is correlated positively to (cocoa) and uncorrelated to (gum). Gum appears affecting sweet taste; the influence of different gums and viscosity levels on sweet taste is complex and has been documented (Calvifio et al., 1993; Barisas et al., 1995; Godshall, 1997). Oral thickness was mainly affected by (gum), but also by (cocoa) (Table 3). Assessors could have been influenced by the chocolate flavor or the dark color in saying that
high cocoa
was
not
also influenced
viscosity explained
and
thicker
(Table oral
86%
3). Linear
thickness
and
This
viscosity
was
correlations
vs. instrumental
85%
of
the
variance,
respectively.
Explained
a, (const)
bl (gum)
bz (cocoa)
Undisolved chocolate Chocolate in suspension Precipitated White spots Mottled surface
0.86 0.83 0.85 0.84 0.56’
36.54 18.50 7.52 7.35
-5.27 -8.94 -16.49 6.07
9.86 12.67 6.20 -2.96 -
Froth Dark Gloss Reddish Blueish
0.82 0.86 0.73 0.65 0.28’
3.97 43.32 40.31 20.60
8.77
Visual viscosity Thickness Roughness Instrumental viscosity
0.80 0.81 0.74 0.95
37.40 31.79 14.44 21.63
15.64 12.90
Total Milk Chocolate Other aromatics
0.78 0.81 0.86 0.336
41.15 19.13 35.65
Milk Sweet Bitter Chocolate Vanilla Residual
0.78 0.93 0.89 0.90 0.356 0.91
23.36 39.63 20.54 43.48 -
bn (co-*)
Appearance -4.89 6.87 7.03
4.96 3.65
after stirring 5.13
26.29 -2.74 5.96
6.60 12.94 6.67
(R*) for Sensory Descriptors
bn (gum*)
Appearance
2.75 -2.40 Texture 4.64 4.78 4.31 8.65 Aroma
13.78 -12.33 18.71
3.15 Flavor
37.21
in the mouth.
as instrumental
by (cocoa)
of visual
R*
22.67
were
the case
viscosity
TABLE 3. Stepwise Multiple Regression Coefficientsa and Percent Variance Viscosity as a Function of Gum and Cocoa Concentrations Descriptors
samples
entirely
-2.36 4.54 19.56
‘Descriptor = bo + ~IXI + 62 xp + bit XT+ bps xz + btt XI x2 where XI = (GUM)coded bAnalysis of variance of regression was not significant (p > 0.10).
3.80 and x2 = (COCOA)coded.
and Instrumental
b12 (gum*cocoa)
Analysis and Preference Mapping of Powdered Chocolate Milk
Principal component analysis
1
Figure 2 shows PC scores and descriptor correlations for the first two PCs, grouping appearance and texture descriptors. The four repetitions of sample 1 were sufficiently close together to justify only their average being represented, this same criterion was adopted for the rest of the PC and EPM graphs. PC-1 groups undissolved chocolate descriptors on one extreme and froth and white spots on the other, separating samples 3 and 7 from 5 and 6 (Fig. 1). PC-2 groups oral thickness and visual viscosity, separating samples 4, 6 and 7 from 2, 8 and 9. Dark, reddish and roughness are explained by both PC-l and PC-2; the regression equations (Table 3) for these descriptors are only dependent on (cocoa), and by PCs they separate samples with low (cocoa) (2, 3 and 8 in Fig. 2). Regression equations presented in Table 3 showed that most appearance and texture descriptors were influence by both (gum) and (cocoa); this is reflected in PC-1 and PC-2 who also separate samples by a combination of both variables. Figure 3 is for PCs 3 and 4. PC-3 was correlated to precipitated chocolate, separating sample 9 (Fig. 1) from the rest which is explained by its low (gum). Gloss was partially explained by PC-4 (R* = 0.50) but did not separate samples according to gloss experimental values. Figure 4 shows scores and correlations for the first two PCs, grouping aroma and flavor descriptors. Practically all the variation is explained by PC-l, on one extreme chocolate, total aroma, residual and bitter; on the other sweet and milky. Samples were separated on this component according to their (cocoa) (Fig. 1). Samples 2, 3 and 7 had the highest sensory scores on vanilla flavor thus explaining the position of this descriptor on Fig. 4. There is no clear explanation as to why these samples were scored higher in vanilla.
Ei
froth
mott
froth
3 FL
5 ’
O-
r&k undi /
1
$
&pot roug;usP2 prec
dark
red
-0.5-
E 4
-11 -1
I
gloss
I---p-----0
-0.5
0.5
1
PC-3 (11%)
FIG. 3. Third and fourth principal component scores and descriptor correlations for appearance/texture attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
veet 5 ilk_fl
i!Jr
4
23 8
6?
aa
btb !r
nilk ar
-0.5
0
0.5
PC-1 (89%)
Preference mapping: appearance/texture
spoi
ndi
SUS mJ+t
5
0
1
g130ss
$ prec
g
a 2
-l_+-
6
red 4
F
:
3 0.5.
FIG. 4. First and second principal component scores and descriptor correlations for aroma/flavor attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
thick vise
dark rough
20 1
0.5
-0.5 PC-1 (44%)
FIG. 2. First and second principal component scores and descriptor correlations for appearance/texture attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
For global preference vs. appearance/texture PC scores the following was obtained: (a) only 27 out of 60 consumers showed positive ideal points for the elliptical model, and of these none were a better fit than the circular model; (b) all consumers fitted the vector model (p < 0.01) with an average R2 of 0.94 and a minimum R2 of 0.85, (c) 23 out of 30 children and 26 out of 30 adults fitted the circular model (PC 0.01) with positive ideal points with an average R2 of 0.96 and a minimum R2 of 0.84, the remaining consumers also fitted the model but showed negative ideal points; (d) the root mean square (Schiffman et al., 1981) of the circular model and vector models were 0.98 and 0.97, respectively; (e) a between phase analysis of variance (Draper and Smith, 1981;
202
G. Hough, R. Sdnchez
Schiffman et al., 1981) showed the vectorial model (Phase IV) was a better fit (p
saddle type ideal points. Considering an average of the 27 consumers with positive ideal points gave a Phase II ideal point close to the Phase III average consumer (Fig. 5). In the Phase III model, 82% of consumers had positive ideal points, so it made little difference whether only these or all consumers were considered for the average consumer.
Preference mapping: aroma/flavor For aroma and flavor, 89% variance was explained by PC-l. Being a one dimensional attribute it did not seem reasonable to map consumers on more than one external dimension. EPM calculations (Schiffman et al., 1981) were adapted to one dimension for the vectorial and ideal point models; for one dimension the distinction between circular and elliptical models is not valid. All consumers fitted the vectorial model with an average R2 = 0.93 and a minimum R2 = 0.75. Seventy-eight per cent of consumers had their vector pointing in the direction + PC- 1 showing preference towards higher levels of chocolate flavor (Fig. 4). The ideal point model was significant for all consumers &
2
a 1
9 aai C
6
’
7
,8,*@a:
‘C
a
es
8
c
%.a. C
“-ia..:
.._.....__.____________._.........................................
cgy
3
9i c 8
C
2 c -1
adults c = children a =
-2
I
-1
0
Pcu-1
I
1
FIG. 5. External preference map for the circular ideal point model of global preference vs. appearance/texture first two principal components. l, average consumer for the circular model. A, average consumer for the elliptical model considering all consumers. 4, idem only considering consumers who fit the elliptical model.
Analysis and Preference Mapping of Powdered Chocolate Milk
203
35
30
25 I 96 CHILDREN
A ic
96 ADULTS
20
15 5 g
15
0
10
5
0
I -1
2 83
I I
-0.5
;
‘\ 1
0
0.5
I
1
\
1.5
PC-1 FIG. 6. Distribution of consumer’s ideal points along the aroma/flavor first principal component using a one-dimensional model. Numbers represent samples from Fig. 1. x, position of average consumer. the average adult is similar to the average child, yet the ideal points for children are more spread across the sample map. Thus if a PCM were developed for the adults would be generally satisfied average consumer, while a proportion of children would prefer higher or lower levels of chocolate flavor. A developer could think of producing a PCM with (cocoa) in the level of samples 4 and 5 aimed at children who want a strong chocolate flavor. Children who preferred a strong chocolate flavor were the same ones whose ideal points correlated with dark color, ie. those placed high up in the second quadrant of Fig. 5. For the average consumer, results were similar to those obtained by response surface methodology (Hough et al., 1997). The ideal point model showed that ever increasing levels of chocolate, as suggested by the vectorial model, would not be adequate.
CONCLUSIONS A sensory profile was developed for describing reconstituted PCM. This profile was used to study the influence of (gum) and (cocoa) on sensory properties. Most descriptors were highly correlated with the design variables in a logical fashion; for example bitter flavor positively correlated to (cocoa) and not correlated to (gum). Oral thickness was correlated to both (gum) and
ideal point
by measuring viscosity (cocoa), this was confirmed instrumentally. Sensory descriptive analysis proved to be a useful tool in relating formula variations to specific sensory properties of interest to a PCM developer. Principal component analysis showed appearanceltexture to be covered by four dimensions. The first related to undissolved chocolate and the second to viscosity, dark color was explained by both. The third PC was correlated to precipitated chocolate and the fourth had a weak correlation to gloss. Flavor was explained by one dimension with chocolate flavor on one extreme and milk/sweet on the other. Ideal point models of EPM mapping were useful to position individual consumers on the sensory profile. Two dimensional maps were used for appearance/texture and one dimensional for aroma/flavor. Children were more spread out on the sample maps than adults. The preference maps would allow the developer to target the PCM to an average consumer, or towards a group of consumers with specific preferences.
ACKNOWLEDGEMENTS We wish to thank Sancor Coop. Ltda. (Sunchales, Santa Fe, Argentina) for their financial assistance in the development of this project.
204 G.Hough,
R. Sdncht-f
REFERENCES
Jones, P. N., MacFie, H. J. H. and Beilken, S. L. (1989) Use of preference mapping to relate consumer preference to the sensory properties of a processed meat product (tinned cat food). Journal of the Science of Food Agriculture 47, 113-123.
Aguilar, C. A., Hollender, R. and Ziegler, G. R. (1994) Sensory characteristics of milk chocolate with lactose from spraydried milk powder. Journal of Food Science 59, 1239-l 243. Arnold, G. M. and Williams, A. A. (1986) General procrustes analysis in sensory analysis. In Statistical Procedures in Food Research, ed. J. R. Piggott. Elsevier Applied Science, London, pp. 233-253. Barisas, L., Rosett, T. R., Gao, Y., Schmidt, S. J. and Klein, B. P. (1995) Enhanced sweetness in sweetener-NaCl-gum systems. Journal of Food Science 60, 523-527. Beilken, S. L., Eadie, L. M., Griffiths, I., Jones, P. N. and Harris, P. V. (1991) Assessment of the sensory characteristics of meat patties. Journal of Food Science 56, 1470-1475. Bodyfelt, F. W., Tobias, J. and Trout, G. M. (1988) The Sensory Evaluation of Dairy Products. Van Nostrand Reinhold, New York, pp. 154-156. Calviiio, A. M., Garcia-Medina, M. R., Cometto-Mutiiz, J. E. and Rodriguez, M. B. (1993) Perception of sweetness and bitterness in different vehicles. Perception and Psychophysics 54, 75 l-758.
multiCarroll, J. D. (1972) Individual differences and dimensional scaling. In Multidimensional Scaling. Theory and Applications in the Behavioral Sciences, eds R. N. Shepard, A. K. Romney and S. B. Nerlove. Seminar Press, New York, pp. 130-146. Claassen, M. and Lawless, H. T. (1992) Comparison of descriptive terminology systems for sensory evaluation of fluid milk. Journal of Food Science 57, 596-600, 62 1. Draper, N. R. and Smith, H. (1981) Applied Regression Analysis. John Wiley and Sons, New York, pp. 7@107. Gacula Jr, M. C. (1993) Design and Analysis of Sensory Optimiration. Food and Nutrition Press, CT, pp. 143-153. Genstat 5 (1993) Release 3 Reference Manual. Genstat 5 Committee, Rothamstead Experimental Station, Harpenden, UK. Godshall, M. A. (1997) How carbohydrates influence food flavor. Food Technology 51(l), 63-67. Greenhoff, K. and MacFie, H. J. H. (1994) Preference mapping in practice. In Measurement of Food Preferences, eds H. J. H. MacFie and D. M. H. Thomson. Blackie Academic and Professional, Glasgow, pp. 137-147. Helgesen, H., Solheim, R. and Naes, T. (1997) Consumer preference mapping of dry fermented lamb sausages. Food Quality and Prefcrmce 8, 97-109. Hough, G., Sanchez, R., Barbieri, T. and Martinez, E. (1997) Sensory optimization of a chocolate milk formula. Food Quality and Preference, 8, 2 13-22 1.
Kroll, B. J. (1990) Evaluating rating scales for sensory testing with children. Food Technology 44( 1), 78-86. McEwan, J. A. and Thomson, D. M. H. (1989) The repertory grid method and preference mapping in market research: a case study on chocolate confectionery. Food Quality and Preference 1, 5948. McEwan, J. A., Colwill, J. S. and Thomson, D. M. H. (1989a) The application of two free-choice profile methods to investigate the sensory characteristics of chocolate. j’oumal of Sensory Studies 3, 27 l-286.
McEwan, J. A., Moore, J. D. and Colwill, J. S. (1989b) The sensory characteristics of Cheddar cheese and their relationship with acceptability. Journal of the Society of Dairy Technology42,
112-117.
Nute, G. R., MacFie, H. J. H. and Greenhoff, K. (1988) Practical application of preference mapping. In Food Acceptabili~, ed. D. M. H. Thomson. Elsevier Applied Science, London, pp. 377-386. Pagliarini, E., Bertuccioli, M. and Abba, S. (1994) Consumer attitudes and olive oil acceptance: the traditional consumer. Grasasy Aceites 45(1-2), 16-19. Pastor, M. V., Costell, E., Izquierdo, L. and Duran, L. (1996) Sensory profile of peach nectars. Evaluation of assessors and attributes by generalized Procrustes analysis. Food Science and Technology International 2, 219-230.
Raats, M. M. and Shepherd, R. (1992) Free-choice profiling of milks and other products prepared with milks of different fat contents. Journal of Sensory Studies 7, 179203. Schiffman, S. S., Reynolds, M. L. and Young, F. W. (1981) Introduction to Multidimensional Scaling. Academic Press, New York, pp. 253-282. Striven, F. M. and Petty, M. F. (1990) Use of the discriminant function to predict the number of consumers who discriminate. Journal of Sensory Studies 4, 15 l-156. Shepherd, R., Griffiths, N. M. and Smith, K. (1988) The relationship between consumer preferences and trained panelresponses. Journal of Sensory Studies 3, 19-35. Stoer, N. L. and Lawless, H. T. (1993) Comparison of single productscaling and relative-to-reference scaling in sensory evaluation of dairy products. Journal of Sensory Studies 8, 257270.
Tunaley, A., Thomson, D. M. H. and McEwan, J. A. (1988) An investigation of the relationship between preference and the sensory characteristics of nine sweeteners. In Food Acceptability, ed. D. M. H. Thomson. Elsevier Applied Science, London, pp. 387400.
ELSEVIER
PII:
and Prcfcmce Vol. 9, No. 4, pp. 197-204, 1998 1998 Elsevier Science Ltd. A11 rights resewed Printed in Great Britain 0950.3293/98819.00+0.00
50950-3293(97)00064-5
DESCRIPTIVEANALYSISAND EXTERNALPREFERENCE MAPPINGOFPOWDEREDCHOCOLATEMILK Guillermo Hough* & Ricardo Sinchez Instituto Superior Experimental de Tecnologia Alimentaria, 6500 Nueve de Julio, Buenos Aires, Argentina (Accepted 24 October 1997)
descriptive analysis of chocolate milk but their focus was on the fat content of the milk used to prepare it. Stoer and Lawless (1993) used commercial chocolate milks to compare single product scaling and relative-to-reference scaling. &riven and Petty (1990) performed a consumer study with three commercial chocolate milks to test a
ABSTRACT Appearance, texture andjavor descriptors were developedfor powdered chocolate milk. The inruence of cocoa and gum concentrations on these descriptors was studied using stepwise multiple regression. Out of a total of 23 descriptors, four were non-signa&ant. For the sign;J;cant descriptors, the percentage variance explained ranged from 65 to 93%) with an average of 82%. Visual viscosityand oral thickness were correlated with instrumental viscosity. Principal component analysis showed appearance/texture was explained by four principal components, whilst aroma flavor was one-dimensional and depended on cocoa concentration alone. The circular ideal point model was chosen to map consumers on the first two appearance/texture principal components. For aroma flavor, consumers were mapped on a single dimension using an ideal point model. For both appearance/texture and aroma flavor, adults and children had similar averages but the ideal points for children were more spread out on the sample map. 0 1998 Elsevier Science Ltd. All rights reserved
discriminant function. Bodyfelt et al. (1988) mention desirable sensory characteristics and defects to look out for in chocolate milk. There are no reported studies on the influence of formulation ingredients on the sensory profile of chocolate milks. Hough et al. (1997) discussed possible formula variations in PCM. Milk solids were not varied to respect food regulations. Ideal level of sugar was measured and not varied in the response surface methodology experiment to avoid a bulkier product and because consumers could add more sugar if they wished. The two ingredients which were studied in more detail were gum and cocoa concentrations [(gum) and (cocoa), respectively]. External preference mapping is a tool which allows consumer data to be mapped on a multidimensional space, derived from other non-preference (external) data relating to the stimuli. The external space is usually obtained by principal component analysis of descriptive sensory data generated by a trained panel (Greenhoff and MacFie, 1994). External preference mapping is used in market research, yet, due to proprietary reasons, relatively few studies are reported in the literature. Reports
INTRODUCTION
are for: cat food (Jones et al., 1989), cheese (McEwan et chocolate confectionery (McEwan and al. ) 1989b), Thomson, 1989)) household fragrance (Nute et al., 1988)) lamb sausages (Helgesen et al., 1997), meat casserole (Greenhoff and MacFie, 1994), meat patties (Beilken et al., 1991)) olive oil (Pagliarini et at., 1994)) sweeteners (Tunaley et al., 1988) and tomato soups (Shepherd et at., 1988). There are no preference mapping studies for
The most common modes of consumption of chocolate milk are adding a cocoa powder formulation to milk or buying the chocolate milk ready to drink. Hough et al. (1997) optimized the formulation of a powdered chocolate milk (PCM) that only needs water for reconstitution. Sensory descriptive
analysis
of milk
(Claassen
chocolate milk. The objectives
and
Lawless, 1992; Raats and Shepherd, 1992) and milk chocolate (McEwan et al., 1989a; Aguilar et al., 1994) has been reported. Raats and Shepherd (1992) also performed *To whom 31 7-31309; Research Cientifiras,
(a)
to develop
of the present work were: the descriptors
and methodology
for
the sensory profile of PCM, (b) to establish the influence of gum and cocoa varia-
correspondence should be addressed. Fax: + +54e-mail: [email protected]. Both authors are Fellows of the Comision de Investigaciones Buenos Aires, Argentina
(c)
197
tions on the sensory profile, and to relate the sensory profile to consumer ability using external preference mapping.
accept-
198
G. Hough, R. Srinchez
MATERIALSAND
METHODS
Profile development To develop the profile, four samples were used: An initial formula (IF) chosen among formulations provided by industry and found in literature; composition is in Table 1. Details for reconstitution are reported elsewhere (Hough et al., 1997). Powdered cocoa mixed with milk according to packet instructions (Nesquik, Nestle Argentina S.A., Buenos Aires, Argentina). The two highest selling brands in Argentina of UHT chocolate milks ready to drink: Cindor (Mastellone Hnos. S.A., Buenos Aires, Argentina) and Sancor (Sancor Coop. Ltda., Sunchales, Argentina). These four samples were chosen because the study was on variations of chocolate and gum concentrations of IF,thus necessary descriptors would correspond to this sample. The other three were chosen to broaden the range of descriptors should other notes appear during formula variations. The panel consisted of 12 assessors trained in descriptive analysis of a number of products. Assessors received 40 ml of each sample in 100 ml glasses at room temperature, under artificial daylight illumination, and in individual booths. They were instructed to rinse with apple juice between samples. The list of descriptors and definitions were obtained by consensus, i.e. round table discussion of descriptors produced by each assessor. For flavor, standards of suggested descriptors were presented by the panel leader to help panel uniformity. To test the panel on the developed descriptors, the four samples were rated using 1Ocm unstructured lines anchored at the extremes. The samples were presented as before, numbered with 3 digit codes, balancing the order of presentation among assessors. Measurements were done in duplicate on separate days.
Formula variations Once the sensory profile had been developed, the effects of (gum) and (cocoa) on the sensory profile were studied. TABLE 1. Initial Powdered and Reconstituted Chocolate Formulation (corresponds to sample 1 of Fig. 2)
component Non-fat dairy solids Dairy fat Cocoa Sucrose Glucose Xanthan gum Locust bean gum Water
Milk
Powdered (% w/w)
Reconstituted (% w/w)
47.24 9.24 8.70 29.00 3.54 0.116 0.116 2.05
8.14 1.59 1.50 5.00 0.61 0.02 0.02 83.12
In a previous paper the convenience of varying (gum) and (cocoa) geometrically was discussed (Hough et al., 1997), obtaining the formulations shown in Fig. 1, which respond to a P-factor central composite design (Gacula, 1993). The resulting nine samples were analyzed in four sessions. In each session the IF (sample 1 from Fig. 1) was evaluated together with two other samples chosen at random among the eight remaining. The three samples in each session were presented in a balanced order. Thus four repetitions for the central point were obtained.
Consumer tests Sensory acceptability of the nine samples (see Fig. 1) were measured by 60 consumers, divided in 30 11/12-year-old children and 30 18/22-year-old young adults. Samples were evaluated for overall acceptability using a g-point hedonic scale going from ‘super good’ to ‘super bad’ (Kroll, 1990). As with the sensory profile, four sessions were used to evaluate the nine samples.
Instrumental viscosity Apparent viscosity of the nine samples was measured with a Brookfield LVT rotary viscometer (Brookfield Engineering Laboratories Inc., Stoughton, MA), using spindle no. 1 at 60r.p.m. For sample 7 (Fig. 1) spindle no. 2 at 60r.p.m. had to be used. Measurements were at lo-12°C similar to sensory assessments. To avoid thixotropic effects the samples were thoroughly stirred just before measurements.
Statistical analysis The results from testing the panel on the descriptors were analyzed by generalized Procrustes analysis (GPA) followed by principal coordinate analysis (Arnold and Williams, 1986) using a Genstat 5 (1993) procedure. The variation among individual assessments is replaced by a consensus configuration of samples, and assessors are tested for their closeness to the consensus. Assessors close to the consensus are deemed to perform well; those with large residuals perform poorly. Also analysis of variance (ANOVA) for each individual assessor was performed to monitor discrimination ability. Stepwise multiple regression was performed on each descriptor and on instrumental viscosity as a function of formula variations. The criterium to include a term was its p-value < 0.10. Principal component (PC) analysis addresses the correlations among the descriptors and exploits duplication of information to provide informative low-dimensional plots of the data to complement regression analysis. Also principal component scores were used for external preference mapping. For external preference mapping (EPM) the acceptance data for each individual were regressed against the
Analysis and Preference Mapping of Powdered Chocolate Milk
199
3
3
2 I
(0.75)
-1 -
8/ -2-
$
(0.375)
(0.016) I
(0.00s) I
-3-r -3
-2
-1
(O.& +--
(0.1)
0
1
(0.25) I
7
cI
2
L
Gum FIG. 1. Coded gum and cocoa concentrations (uncoded percentage concentrations in reconstituted as design variables in studying their effect on the sensory profile of powdered chocolate milk.
PC scores original
obtained
form
analysis
(Greenhoff
vector
model
one
from
the EPM
or
and
which
more
decreasing
the descriptive
algorithm MacFie,
implies
characteristic
is present,
if it is a negative
characteristic,
the characteristic
is present
point
model
ulus space which
implies there
from
ideal
the ideal
regardless instead
point
of circular do not
For Phase
the dimensions. were
taken
(1981)
point
a
that
Carroll
III
would
(1972)
IV EPM
between
calculations
Schiffman
et al.
5 (1993).
RESULTS
descriptors
are listed low-nil
(1) Undissolved chocolate on surface (undi) Chocolate in suspension (susp) Precipitated chocolate (prec) White spots on surface (spot) Mottled surface (mott)
Aroma (4) Total (aroma) Milky (milk-ar) Chocolate (choc-ar) Other aromatics
Appearance after stirring (2) Froth Dark Gloss Reddish (red) Blueish
Flavor (5) Milky (milk-fl) Sweet Bitter Chocolate (choc-fl) Vanilla (van) Residual (resid)
Texture Visual viscosity (3): measured visually by pouring sample from one glass to another (vise) Thickness (6): degree of thickness perceived by manipulating sample in the mouth (thick) Roughness (7): degree of roughness perceived by manipulating sample in the mouth (rough)
GPA
and
in Table
definitions
2. The
to high-extreme.
showed
that
all assessors
had
sufficiently
small
residuals (range = 0.12-0.25) for appearance and texture. For aroma and flavor one of the 12 assessors had a high residual (0.43 vs. a range for the remaining assessors of
Profile development The
Appearance
contours
to the acceptance
and
in Genstat
is the
distance
of the stim-
axes are rotated and
used
TABLE 2. Descriptors used for Sensory Profiling of Chocolate Milk (order of evaluation is indicated in parenthesis)
be constant
II has elliptical equally
II, III
the stim-
Phase
are in parentheses)
the less The ideal
at a fixed
thus the dimensions
and programmed
more
of a characteristic,
the acceptance Phase
within
point.
such
I the contour
from
or
the
it is liked.
strength
contribute
Phase
increasing
like an off-flavor,
an ideal
ones,
IV is the
it is liked; alternatively
at some
model
of direction.
ulus space model.
with
point
of
between
Thus
the more
is an ideal
will equate
circular
that
are
space.
the more
Phase
relationship
which
the stimulus
In its
four phases
1994).
a direct
characteristics
across
analysis.
includes
product
developed
end anchors
by the panel
of the scales
were
0.24-0.35) nate
and
analysis.
stantially
from
was an outlier Her
individual
average.
on the principal loadings
differed
coordisub-
200
G. Hough, R. Sdnchez
Pastor et al. (1996) suggested accepting p-values < 0.30 when using ANOVA to monitor assessor’s discrimination on individual descriptors. The assessor who was an outlier in the aroma/flavor GPA analysis had p-values >0.30 on the following descriptors: total aroma, bitter, chocolate flavor, vanilla and residual. She was excused from participating in the descriptive analysis of formula variations. Of the remaining assessors who were not outliers in the aroma/flavor GPA analysis, seven had all pvalues < 0.30, two had p-values > 0.30 for one descriptor, and two had p-values >0.30 for two descriptors. These assessors were retained.
Formula variations Data averaged over assessors were used for the regression analysis on (gum) and (cocoa) variations; results are in Table 3. Analysis of variance of the regression was not significant (p > 0.10) for mottled surface, vanilla, blueish and other aromatics. Mean intensities of these last two descriptors for all samples were less than 5 on a O-100 sensory scale; they were present in samples used for profile development, but not in those used to study formula
variations. These descriptors were eliminated from the PC analysis and consequently from EPM. For the rest of the descriptors (Table 3) the percent variance explained (R*) was > 65% and the p-value for the regression ANOVA was ~0.004, thus showing an adequate fit of design variables to sensory response. For each descriptor, terms included in the model were as expected; e.g. undissolved chocolate on surface is correlated negatively with (gum) and positively with (cocoa); bitter taste is correlated positively to (cocoa) and uncorrelated to (gum). Gum appears affecting sweet taste; the influence of different gums and viscosity levels on sweet taste is complex and has been documented (Calvifio et al., 1993; Barisas et al., 1995; Godshall, 1997). Oral thickness was mainly affected by (gum), but also by (cocoa) (Table 3). Assessors could have been influenced by the chocolate flavor or the dark color in saying that
high cocoa
was
not
also influenced
viscosity explained
and
thicker
(Table oral
86%
3). Linear
thickness
and
This
viscosity
was
correlations
vs. instrumental
85%
of
the
variance,
respectively.
Explained
a, (const)
bl (gum)
bz (cocoa)
Undisolved chocolate Chocolate in suspension Precipitated White spots Mottled surface
0.86 0.83 0.85 0.84 0.56’
36.54 18.50 7.52 7.35
-5.27 -8.94 -16.49 6.07
9.86 12.67 6.20 -2.96 -
Froth Dark Gloss Reddish Blueish
0.82 0.86 0.73 0.65 0.28’
3.97 43.32 40.31 20.60
8.77
Visual viscosity Thickness Roughness Instrumental viscosity
0.80 0.81 0.74 0.95
37.40 31.79 14.44 21.63
15.64 12.90
Total Milk Chocolate Other aromatics
0.78 0.81 0.86 0.336
41.15 19.13 35.65
Milk Sweet Bitter Chocolate Vanilla Residual
0.78 0.93 0.89 0.90 0.356 0.91
23.36 39.63 20.54 43.48 -
bn (co-*)
Appearance -4.89 6.87 7.03
4.96 3.65
after stirring 5.13
26.29 -2.74 5.96
6.60 12.94 6.67
(R*) for Sensory Descriptors
bn (gum*)
Appearance
2.75 -2.40 Texture 4.64 4.78 4.31 8.65 Aroma
13.78 -12.33 18.71
3.15 Flavor
37.21
in the mouth.
as instrumental
by (cocoa)
of visual
R*
22.67
were
the case
viscosity
TABLE 3. Stepwise Multiple Regression Coefficientsa and Percent Variance Viscosity as a Function of Gum and Cocoa Concentrations Descriptors
samples
entirely
-2.36 4.54 19.56
‘Descriptor = bo + ~IXI + 62 xp + bit XT+ bps xz + btt XI x2 where XI = (GUM)coded bAnalysis of variance of regression was not significant (p > 0.10).
3.80 and x2 = (COCOA)coded.
and Instrumental
b12 (gum*cocoa)
Analysis and Preference Mapping of Powdered Chocolate Milk
Principal component analysis
1
Figure 2 shows PC scores and descriptor correlations for the first two PCs, grouping appearance and texture descriptors. The four repetitions of sample 1 were sufficiently close together to justify only their average being represented, this same criterion was adopted for the rest of the PC and EPM graphs. PC-1 groups undissolved chocolate descriptors on one extreme and froth and white spots on the other, separating samples 3 and 7 from 5 and 6 (Fig. 1). PC-2 groups oral thickness and visual viscosity, separating samples 4, 6 and 7 from 2, 8 and 9. Dark, reddish and roughness are explained by both PC-l and PC-2; the regression equations (Table 3) for these descriptors are only dependent on (cocoa), and by PCs they separate samples with low (cocoa) (2, 3 and 8 in Fig. 2). Regression equations presented in Table 3 showed that most appearance and texture descriptors were influence by both (gum) and (cocoa); this is reflected in PC-1 and PC-2 who also separate samples by a combination of both variables. Figure 3 is for PCs 3 and 4. PC-3 was correlated to precipitated chocolate, separating sample 9 (Fig. 1) from the rest which is explained by its low (gum). Gloss was partially explained by PC-4 (R* = 0.50) but did not separate samples according to gloss experimental values. Figure 4 shows scores and correlations for the first two PCs, grouping aroma and flavor descriptors. Practically all the variation is explained by PC-l, on one extreme chocolate, total aroma, residual and bitter; on the other sweet and milky. Samples were separated on this component according to their (cocoa) (Fig. 1). Samples 2, 3 and 7 had the highest sensory scores on vanilla flavor thus explaining the position of this descriptor on Fig. 4. There is no clear explanation as to why these samples were scored higher in vanilla.
Ei
froth
mott
froth
3 FL
5 ’
O-
r&k undi /
1
$
&pot roug;usP2 prec
dark
red
-0.5-
E 4
-11 -1
I
gloss
I---p-----0
-0.5
0.5
1
PC-3 (11%)
FIG. 3. Third and fourth principal component scores and descriptor correlations for appearance/texture attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
veet 5 ilk_fl
i!Jr
4
23 8
6?
aa
btb !r
nilk ar
-0.5
0
0.5
PC-1 (89%)
Preference mapping: appearance/texture
spoi
ndi
SUS mJ+t
5
0
1
g130ss
$ prec
g
a 2
-l_+-
6
red 4
F
:
3 0.5.
FIG. 4. First and second principal component scores and descriptor correlations for aroma/flavor attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
thick vise
dark rough
20 1
0.5
-0.5 PC-1 (44%)
FIG. 2. First and second principal component scores and descriptor correlations for appearance/texture attributes. Numbers represent samples from Fig. 1; for descriptor abbreviations see Table 2.
For global preference vs. appearance/texture PC scores the following was obtained: (a) only 27 out of 60 consumers showed positive ideal points for the elliptical model, and of these none were a better fit than the circular model; (b) all consumers fitted the vector model (p < 0.01) with an average R2 of 0.94 and a minimum R2 of 0.85, (c) 23 out of 30 children and 26 out of 30 adults fitted the circular model (PC 0.01) with positive ideal points with an average R2 of 0.96 and a minimum R2 of 0.84, the remaining consumers also fitted the model but showed negative ideal points; (d) the root mean square (Schiffman et al., 1981) of the circular model and vector models were 0.98 and 0.97, respectively; (e) a between phase analysis of variance (Draper and Smith, 1981;
202
G. Hough, R. Sdnchez
Schiffman et al., 1981) showed the vectorial model (Phase IV) was a better fit (p
saddle type ideal points. Considering an average of the 27 consumers with positive ideal points gave a Phase II ideal point close to the Phase III average consumer (Fig. 5). In the Phase III model, 82% of consumers had positive ideal points, so it made little difference whether only these or all consumers were considered for the average consumer.
Preference mapping: aroma/flavor For aroma and flavor, 89% variance was explained by PC-l. Being a one dimensional attribute it did not seem reasonable to map consumers on more than one external dimension. EPM calculations (Schiffman et al., 1981) were adapted to one dimension for the vectorial and ideal point models; for one dimension the distinction between circular and elliptical models is not valid. All consumers fitted the vectorial model with an average R2 = 0.93 and a minimum R2 = 0.75. Seventy-eight per cent of consumers had their vector pointing in the direction + PC- 1 showing preference towards higher levels of chocolate flavor (Fig. 4). The ideal point model was significant for all consumers &
2
a 1
9 aai C
6
’
7
,8,*@a:
‘C
a
es
8
c
%.a. C
“-ia..:
.._.....__.____________._.........................................
cgy
3
9i c 8
C
2 c -1
adults c = children a =
-2
I
-1
0
Pcu-1
I
1
FIG. 5. External preference map for the circular ideal point model of global preference vs. appearance/texture first two principal components. l, average consumer for the circular model. A, average consumer for the elliptical model considering all consumers. 4, idem only considering consumers who fit the elliptical model.
Analysis and Preference Mapping of Powdered Chocolate Milk
203
35
30
25 I 96 CHILDREN
A ic
96 ADULTS
20
15 5 g
15
0
10
5
0
I -1
2 83
I I
-0.5
;
‘\ 1
0
0.5
I
1
\
1.5
PC-1 FIG. 6. Distribution of consumer’s ideal points along the aroma/flavor first principal component using a one-dimensional model. Numbers represent samples from Fig. 1. x, position of average consumer. the average adult is similar to the average child, yet the ideal points for children are more spread across the sample map. Thus if a PCM were developed for the adults would be generally satisfied average consumer, while a proportion of children would prefer higher or lower levels of chocolate flavor. A developer could think of producing a PCM with (cocoa) in the level of samples 4 and 5 aimed at children who want a strong chocolate flavor. Children who preferred a strong chocolate flavor were the same ones whose ideal points correlated with dark color, ie. those placed high up in the second quadrant of Fig. 5. For the average consumer, results were similar to those obtained by response surface methodology (Hough et al., 1997). The ideal point model showed that ever increasing levels of chocolate, as suggested by the vectorial model, would not be adequate.
CONCLUSIONS A sensory profile was developed for describing reconstituted PCM. This profile was used to study the influence of (gum) and (cocoa) on sensory properties. Most descriptors were highly correlated with the design variables in a logical fashion; for example bitter flavor positively correlated to (cocoa) and not correlated to (gum). Oral thickness was correlated to both (gum) and
ideal point
by measuring viscosity (cocoa), this was confirmed instrumentally. Sensory descriptive analysis proved to be a useful tool in relating formula variations to specific sensory properties of interest to a PCM developer. Principal component analysis showed appearanceltexture to be covered by four dimensions. The first related to undissolved chocolate and the second to viscosity, dark color was explained by both. The third PC was correlated to precipitated chocolate and the fourth had a weak correlation to gloss. Flavor was explained by one dimension with chocolate flavor on one extreme and milk/sweet on the other. Ideal point models of EPM mapping were useful to position individual consumers on the sensory profile. Two dimensional maps were used for appearance/texture and one dimensional for aroma/flavor. Children were more spread out on the sample maps than adults. The preference maps would allow the developer to target the PCM to an average consumer, or towards a group of consumers with specific preferences.
ACKNOWLEDGEMENTS We wish to thank Sancor Coop. Ltda. (Sunchales, Santa Fe, Argentina) for their financial assistance in the development of this project.
204 G.Hough,
R. Sdncht-f
REFERENCES
Jones, P. N., MacFie, H. J. H. and Beilken, S. L. (1989) Use of preference mapping to relate consumer preference to the sensory properties of a processed meat product (tinned cat food). Journal of the Science of Food Agriculture 47, 113-123.
Aguilar, C. A., Hollender, R. and Ziegler, G. R. (1994) Sensory characteristics of milk chocolate with lactose from spraydried milk powder. Journal of Food Science 59, 1239-l 243. Arnold, G. M. and Williams, A. A. (1986) General procrustes analysis in sensory analysis. In Statistical Procedures in Food Research, ed. J. R. Piggott. Elsevier Applied Science, London, pp. 233-253. Barisas, L., Rosett, T. R., Gao, Y., Schmidt, S. J. and Klein, B. P. (1995) Enhanced sweetness in sweetener-NaCl-gum systems. Journal of Food Science 60, 523-527. Beilken, S. L., Eadie, L. M., Griffiths, I., Jones, P. N. and Harris, P. V. (1991) Assessment of the sensory characteristics of meat patties. Journal of Food Science 56, 1470-1475. Bodyfelt, F. W., Tobias, J. and Trout, G. M. (1988) The Sensory Evaluation of Dairy Products. Van Nostrand Reinhold, New York, pp. 154-156. Calviiio, A. M., Garcia-Medina, M. R., Cometto-Mutiiz, J. E. and Rodriguez, M. B. (1993) Perception of sweetness and bitterness in different vehicles. Perception and Psychophysics 54, 75 l-758.
multiCarroll, J. D. (1972) Individual differences and dimensional scaling. In Multidimensional Scaling. Theory and Applications in the Behavioral Sciences, eds R. N. Shepard, A. K. Romney and S. B. Nerlove. Seminar Press, New York, pp. 130-146. Claassen, M. and Lawless, H. T. (1992) Comparison of descriptive terminology systems for sensory evaluation of fluid milk. Journal of Food Science 57, 596-600, 62 1. Draper, N. R. and Smith, H. (1981) Applied Regression Analysis. John Wiley and Sons, New York, pp. 7@107. Gacula Jr, M. C. (1993) Design and Analysis of Sensory Optimiration. Food and Nutrition Press, CT, pp. 143-153. Genstat 5 (1993) Release 3 Reference Manual. Genstat 5 Committee, Rothamstead Experimental Station, Harpenden, UK. Godshall, M. A. (1997) How carbohydrates influence food flavor. Food Technology 51(l), 63-67. Greenhoff, K. and MacFie, H. J. H. (1994) Preference mapping in practice. In Measurement of Food Preferences, eds H. J. H. MacFie and D. M. H. Thomson. Blackie Academic and Professional, Glasgow, pp. 137-147. Helgesen, H., Solheim, R. and Naes, T. (1997) Consumer preference mapping of dry fermented lamb sausages. Food Quality and Prefcrmce 8, 97-109. Hough, G., Sanchez, R., Barbieri, T. and Martinez, E. (1997) Sensory optimization of a chocolate milk formula. Food Quality and Preference, 8, 2 13-22 1.
Kroll, B. J. (1990) Evaluating rating scales for sensory testing with children. Food Technology 44( 1), 78-86. McEwan, J. A. and Thomson, D. M. H. (1989) The repertory grid method and preference mapping in market research: a case study on chocolate confectionery. Food Quality and Preference 1, 5948. McEwan, J. A., Colwill, J. S. and Thomson, D. M. H. (1989a) The application of two free-choice profile methods to investigate the sensory characteristics of chocolate. j’oumal of Sensory Studies 3, 27 l-286.
McEwan, J. A., Moore, J. D. and Colwill, J. S. (1989b) The sensory characteristics of Cheddar cheese and their relationship with acceptability. Journal of the Society of Dairy Technology42,
112-117.
Nute, G. R., MacFie, H. J. H. and Greenhoff, K. (1988) Practical application of preference mapping. In Food Acceptabili~, ed. D. M. H. Thomson. Elsevier Applied Science, London, pp. 377-386. Pagliarini, E., Bertuccioli, M. and Abba, S. (1994) Consumer attitudes and olive oil acceptance: the traditional consumer. Grasasy Aceites 45(1-2), 16-19. Pastor, M. V., Costell, E., Izquierdo, L. and Duran, L. (1996) Sensory profile of peach nectars. Evaluation of assessors and attributes by generalized Procrustes analysis. Food Science and Technology International 2, 219-230.
Raats, M. M. and Shepherd, R. (1992) Free-choice profiling of milks and other products prepared with milks of different fat contents. Journal of Sensory Studies 7, 179203. Schiffman, S. S., Reynolds, M. L. and Young, F. W. (1981) Introduction to Multidimensional Scaling. Academic Press, New York, pp. 253-282. Striven, F. M. and Petty, M. F. (1990) Use of the discriminant function to predict the number of consumers who discriminate. Journal of Sensory Studies 4, 15 l-156. Shepherd, R., Griffiths, N. M. and Smith, K. (1988) The relationship between consumer preferences and trained panelresponses. Journal of Sensory Studies 3, 19-35. Stoer, N. L. and Lawless, H. T. (1993) Comparison of single productscaling and relative-to-reference scaling in sensory evaluation of dairy products. Journal of Sensory Studies 8, 257270.
Tunaley, A., Thomson, D. M. H. and McEwan, J. A. (1988) An investigation of the relationship between preference and the sensory characteristics of nine sweeteners. In Food Acceptability, ed. D. M. H. Thomson. Elsevier Applied Science, London, pp. 387400.