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Sensory optimization of a powdered chocolate milk formula
Food Q&v
and Prcfcmce
Vol. 8, No. 3, pp. 213-221, 1997 0 1997 Else&r Science Ltd All rights reserved. Printed in Great Britain
PII:SO950-3293(96)00051-l
ELSEVIER
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SENSORYOPTlMlZATlONOFAPOWDEREDCHOCOLATEMlLK FORMULA Guillermo Hough,* Ricardo Sbnchez, Teresa Barbieri & Edgardo Martinez Instituto Superior Experimental de Tecnologia Alimentaria, 6500 Nueve de Julio, Buenos Aires, Argentina (Accepted
1 November
When optimizing a formulation, a common goal is to maximize sensory acceptability. PCM would mainly be targeted to children and adolescents. There are a limited number of studies reporting the use of acceptability scales
ABSTRACT Four experiments were carried out to optimize the formulation of a powdered chocolate milk. The jrst of these compared facial vs. verbal-numeric self-administered scales when measuring sensory acceptability of di$erent attributes among il-year-old children. Both scales were equivalent. In a second experiment, magnitude estimation was performed to analyze sensory viscosity as a function of gum concentration, and chocolate flavor as a function of cocoa concentration. Steven’s Law exponents were: 0.37for uiscosity and 0.78for chocolateJlavor. Third, by using a minimum bias method, the optimum level of sugar concentration resulted in 5.0% for adults and 6.0% for children. In the fourth experiment
with children. The g-point hedonic scale is a widely used tool (Stone and Sidel, 1985) originally developed for adults and later adapted to children (Kroll, 1990). Facial scales have been used with children, although Stone and Side1 (1985) point out difficulties in their use. Kimmel et al. (1994) successfully used a combined facial and verbal scale with 8-lo-year-old children guided by an interviewer. For variations of sweetness in an orange drink Kroll (1990) showed that 8-lo-year-old children could use a self-administered g-point hedonic scale for overall acceptability. Moskowitz (1985, 1994) presents case studies where children use O-100 anchored scales guided by an interviewer, and with younger children use a ‘Snoopy’ facial scale. There are no studies on children using selfadministered scales to measure acceptability of different attributes like appearance, texture, flavor and overall acceptability.
gum and cocoa concentration were optimized using response surface methodology. Based on Steven’s Law exponents, levels were varied geometrically. Contour plots for appearance, texture, flavor and overall preference are presented for children and adults. No unique formula would satisfy both age groups for all attributes. 0 1997 Elsevier Science Ltd
In optimizing the PCM formulation, quantities of several ingredients can be modified to obtain maximum sensory acceptability. Response surface methodology (RSM) is an effective statistical method to investigate sensory characteristics when a number of ingredients are to be tested simultaneously (Henika, 1982). For PCM the objective of RSM is to predict optimal sensory acceptability by applying a formulation design where concentrations are varied according to a predefined
INTRODUCTION
Chocolate milk is a widely consumed most common modes of consumption
1996)
dairy product. The are adding a cocoa
pattern. Concentration levels do not usually affect sensory perception linearly, Steven’s Law (Moskowitz, 1977) is used to relate sensory perception to changes in a physical magnitude, in our case, concentration:
powder formulation to milk or buying the chocolate milk ready to drink. Powdered chocolate milk (PCM) that only needs water for reconstitution has interesting applications as a convenience beverage for the home and outdoor use (picnics and camping). PCM is marketed, e.g. by Cadbury’s in the United Kingdom (Premier Brands, Birmingham) or by DOS Alamos in Chile (Panguipulli, Valdivia), yet formulations have not been published and neither are there published reports on optimization of liquid chocolate milk formulations.
Sensory = k.(Concentration)”
Linear variations in concentration, normally used in RSM, could lead to exaggerated variations in sensory perception if n > 1 or small sensory variations (perhaps not significantly different) if n < 1. Thus to conduct sensory optimization by RSM, previous knowledge of Steven’s Law exponent can be of help in choosing concentration levels.
Research fellow of the Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. 213
214 G. Houghet al. The objectives
(4 (b) (4
of the present work were:
one to include vanilla flavoring. By informal tasting among colleagues an initial formula (IF) was made up which had acceptable levels of color, texture, chocolate flavor and sweetness. A group of 12 1 l-year-old children also tried the IF together with two commercial fluid chocolate milks: 7 of them ranked the IF first and 5 ranked it second. The IF, the starting point for RSM optimization, is shown in Table 1. PCM was reconstituted so as to obtain 8.14% non-fat dairy solids in the reconstituted chocolate milk. When the IF was modified, e g. to increase cocoa concentration, the PCM composition and water for reconstitution were recalculated to maintain always the 8.14% non-fat dairy solids in the reconstituted PCM. The reconstitution was done by placing the powder over the water and
test different hedonic scales to be used with 1 lyear-old children, conduct magnitude estimation tests to estimate Steven’s Law exponent for selected PCM ingredients, and use RSM methodology to optimize PCM formulation based on sensory acceptability.
As results from one experiment influence the design of the following, each experiment, with results included, is presented sequentially.
EXPERIMENTl:HEDONIC SCALETO BE USED CHILDREN
WITH
1. Initial Powdered and Reconstituted Milk Formulation
TABLE
Initial formula
Non-fat dairy solids Dairy fat Cocoa
A major dairy company provided us with formulations for fluid chocolate milk and PCM. A fluid chocolate milk formula was also obtained from relevant literature (Tressler and Sultan, 1975). The major variations were in the type and quantity of stabilizing gums, and in the quantity of cocoa. The literature formula was the only
Reconstituted (% w/w)
Powdered (% w/w)
Comwnent
Sucrose
Glucose Xanthan gum Locust bean gum Water
Chocolate
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
CHOCOLATE MILK PREFEREtxE
NAME: -
You
. .
..I..........................
will
- Considering following
receive
3 glasses
everything, scale:
with
please
chocolate score
each
milk sample
using
the
9 - Super good
8 - Really good 7 - Good 6 - Just a little good 5 - Maybe good or maybe bad 4 - Just a little bad 3 - Bad 2 - Really bad 1 - Super bad
II
SAMPLE
I
II
815
I
SCORE
11
FIG. 1. Score sheet used by assessorsto measure overall preference for different samples of chocolate milk using a verbal numeric scale.
215
Sensory Optimization of a Powdered Chocolate Milk
CHOCOLATE MILK PREFERENCE
NAME:................................ -
YOU will
receive
- Considering
3 glasses
everything,
with
please
chocolate mark
milk
the appropriate
face.
FIG. 2. Score sheet used by assessors to measure overall preference for different samples of chocolate milk using a facial scale.
mixing
with an electric
tion was between
hand mixer for 30 sec. Prepara-
flavor evaluations. The
Sensory tests and experimental design
facial
re-scaled g-point
The
two types of scales that were tested are shown in
Figs 1 and 2. The chocolate IF (see Table
l),
and
(Nestle
Nesquik
Argentina) Figure dure.
milks used for the test were:
IF with
prepared
1.6%
according
3 shows a diagram
Sixty-four
chocolate scale first, days. Then
children,
participated
also performed verbal-numeric
duplicate
Group
duplicate
they received
Buenos
Aires,
to packet instructions.
into two groups.
performing
S.A.,
of the experimental
1 l-year-old
milk regularly,
were divided
sugars and 1% cocoa,
Argentina
who
measures.
proce-
repeated between
were available
7 points
so these
results
by 9/7 to compare
if
measures
model. variables,
subjects
Lawless and Malone
Groups and
variables.
and
milks
were
with
scale. The data were analyzed
subjects
were within
scales
and
the
using a were
duplicates
For similar
designs
( 1986) or Oude Ophius
( 1994).
see
Results The
(ANOVA)
on different scale and
Group 2 received the
Children received instructions on the attributes and scale to be used and were then conducted to individual booths. They received 50 ml of each of the three samples in 80 ml glasses coded with 3-digit random numbers, and overall preference.
and crackers
consumed
scale first and then the facial scale.
they were asked to evaluate
hedonic
1 used the facial
measures
scale had
by multiplying
in the test. They
the verbal-numeric
Water
wanted.
20 and 30 min before tasting.
The three
samples were removed and fresh ones, with different codes, were presented for appearance, oral texture and
main
numeric,
result showed
obtained
by
analysis
that both scales, facial
were equivalent
of
variance
and verbal-
for all attributes
(p > 0.20).
Average scores for each scale are in Table 2. It can be observed in Table 2 that the children discriminate
significantly
among
the samples.
did
As expec-
ted, preference for the low sugar-low cocoa sample was significantly lower in texture, flavor and overall preference. The IF had high preference scores showing it was a good starting point for RSM. Differences in texture between the IF and the low sugar-low cocoa were not high as the gum concentrations were kept equal, yet preference
scores were very different
216
G. Hough et al.
rFacia1 first (duplicate)
I
- Group 1 (32 children)
64
L
Verbal-Numeric (duplicate)
second
7
Verbal-Numeric (duplicate)
first
children
. Group 2 ( 32 children
I ) L
FIG. 3. Experimental
procedure
used to compare
Facial second (duplicate)
facial scales with verbal-numeric
scales in 1 l-year-old
TABLE 2. Average
Numeric
Hedonic
Chocolate
Preference Scores (1 = super bad, 9 = super good) for the Three Chocolate Scales with 1 l-year-old Children
milks
Initial formulation Low sugar-low cocoa Nesquik ‘*‘JDifferent
orally,
8.5 6.4 5.9
the sample This
texture
8.4” 6.0b 5.8b
i.e. and halo
visually
the
consequently led
instead
significant
8.0 3.4 7.6
differences
disliked rated
the
(4
of
in
the
measuring rest
of the
8.3 2.1 7.9
8.4 3.2 7.5
8.4” 2.6b 7.7”
chocolate
flavor
concentrations
0.67,
1.00,
1.50,
(b) viscosity (50%
the low sugar-low
cocoa
scale: 2.1 vs 3.1 respectively
This might indicate the products
the
following
2.25
and
3.37%
PCM:
(geometric
0.056,
PCM: 0.078,
gum concentrations locust
0.010, 0.11,
bean
gum)
0.015, 0.020, 0.15%
in
0.029,
(geometric
factor 21 1.4).
sample
lower scores with the facial scale than the ver-
bal-numeric
using
gum+ 50%
0.04,
but
8.0” 3.6b 7.6’
in the reconstituted
using the following
xanthan
The only significant interaction was Sample x Scale for flavor. The IF and Nesquik received equivalent scores scales,
8.1 3.7 7.7
factor 11 1.5).
the reconstituted
for both
7.9 3.5 7.4
intensity
cocoa
experiments.
received
Overall preference Facial Verbal Average
(Jo< 0.05).
it low in texture
us to consider
of orally
flavor
Milks Used to Test Facial and Verbal-
Flavor Facial Verbal Average
8.0” 3.2b 7.7”
et al., 1991) with flavor
children
effect
8.1 2.9 7.8
2). As texture was evaluated
(see Table
a halo effect (Meilgaard
is suggested, also.
8.3 5.6 5.7
letters in each column indicate
for this attribute
Texture Facial Verbal Average
Appearence Facial Verbal Average
children.
(see Table
2).
lower scores for the facial scale when
are low in preference.
Sensory
evaluations
were performed
by a 10 member
trained panel who had previous experience
in magnitude
estimation
presented
measurements.
Samples
were
in
random order in 80 ml glasses coded with 3-digit random numbers. For chocolate flavor the glasses were placed in a box and under red light to mask color dif-
EXPERIMENT ESTIMATION
2:MAGNITUDE
uated
due to different amounts of cocoa. Water and were used as neutralizers. Viscosity was evalvisually
and
orally.
For
the
visual
evaluation
assessors poured the milk from one glass to another observing flow and turbulence. Oral viscosity was eval-
Samples and sensory tests Fixed modulus magnitude 1977) were performed on:
ferences crackers
estimation
tests (Moskowitz,
uated by taking a generous mouthful of milk and manipulating it in the mouth. Chocolate flavor, visual and oral viscosity were evaluated separately on three
Sensory Optimization of a Powdered Chocolate Milk TABLE 3. Steven’s Law Exponents, 95% Confidence Intervals, and Percent Variance Explained by Regression of Magnitude Estimation Results
Exponent 95Ya con6dence
s--Y
Chocolate flavor Visual viscosity Oral viscosity
different Mori
days.
0.78 0.37 0.37
Results
were
lated by consumers who prefer very sweet products. Before introducing sugar concentration as an extra variable in RSM, the ideal level of this ingredient was measured.
YO variance erplained
interval
attribute
hO.12 * 0.04 f 0.08
98 98 92
evaluated
as detailed
Sensory method
by
et al. ( 1984).
Results Table
3 gives Steven’s
linear
regression
The relatively
Law exponents
large
confidence
estimated
from the
vs log(concentration)
of log(sensory)
interval
.
for the chocolate
flavor exponent is due to only five experimental points being used. The exponents for oral and visual viscosity published by other authors (see Table 4) are in the same range as our values for chocolate milk, even though the physical measurements for values in Table 4 were instrumental viscosity and not gum concentration as in our case. Zamora (1995) showed standard deviations for oral viscosity assessments were greater than for visual assessments. Our conclusions are similar (Table 3). No literature values were found to compare with the exponent for chocolate flavor intensity.
Introduction When deciding on which ingredient quantities to optimize, milk solids were kept constant to respect food regulation limitations (Codigo Alimentario Argentino 1992). When deriving IF (see Experiment 1), sugar was between 5 and 6% in the different formulations consulted. It is also an ingredient which can be manipufor Steven’s
Law
Exponents
Type of evaluation
Product
Exponent
Calvirio (1982)
Visual
Silicones
0.38
Christensen (1987)
Oral Visual
Na alginate Solutions
0.34 0.39
Oral Visual
Silicones
0.42 0.42
Oral Visual
Starch Pastes
Autborls
and Casper
Stevens and Guirao (1964) Zamora
( 1995)
Sugar concentrations used to measure the ideal level were: 1, 2, 4, 8 and 16% in the reconstituted PCM; with the rest of the ingredients as in the IF except water. A minimum bias procedure published by Conner et al. (1987) was used. Each sample was presented individually and rated on a non-structured 100 mm scale anchored ‘not at all sweet’ at the left end, ‘ideal sweetness’ in the middle and ‘extremely sweet’ at the right end. The first sample presented was 4% sugar concentration. After rating this the assessor handed back the glass and the rating slip. Panel leaders were instructed to choose the second sample so that it would be likely to be rated on the other side of the ideal mid-point regarding the first sample, but not at an extreme. Subsequent samples were to be selected for presentation in a way that would be likely to alternate responses on either side of the mid-point, while also balancing the responses around it such that the mean of the session of responses was close to the midpoint. For each assessor a linear regression of sensory scores vs log sugar concentration was calculated. Ideal sugar level was derived from the regression by calculating the concentration corresponding to the mid-point of the scale. The test was performed by 30 1 l-year-old children and by 30 adults between 18 and 40 years old, all of whom consumed chocolate milk at least once a month.
Results
EXPERIMENT3:IDEALSUGAR LEVEL
TABLE 4. Literature Values when Measuring Viscosity
2 17
0.40 0.41
Mean f standard deviation ideal sugar levels were: 6.0 f 2.2 for children, 5.0 f 1.5 for adults, and 5.5 f 1.9 grouping all assessors together. A Student’s ttest indicated that children had a significantly higher ideal sugar level than adults. Sugar concentration was kept at 5% (IF, Table 1) and not included as an RSM independent variable due to: (a) increase in sugar concentration would mean a bulkier PCM, (b) consumers can add more sugar if they please, (c) RSM is simplified if the number of independent variables are kept to a minimum and (d) sugar concentration in the IF (Table 1) was close to the mean ideal values.
EXPERIMENT4:RESPONSE SURFACEMETHODOLOGY Choice of levels Having discarded milk solids and sugar, the two variables to optimize by RSM were cocoa and gum concentration. A two-factor central composite design was used (Gacula, 1993).
218
G. Hough et al.
As regards the levels, success in defining the optimum product depends on the selection of appropriate factor levels within given limitations (Giovanni, 1983). Most authors have used arithmetic variations of independent variables for RSM optimization (Henika, 1982; Fishken, 1988; Schantz and Bowers, 1993; Matulis et al., 1995). Non-arithmetical variations have been used (Abdullah et al., 1993; Mora-Escobedo et al., 1991) but with no reasons given. Table 5 shows coded and uncoded concentrations and predicted sensory viscosity using Steven’s Law, assuming arithmetical and geometric variations in gum concentration. When changing gum concentration from 0.04 to 0.06%, changes in predicted sensory viscosity only changed from 100 to 116. It is doubtful whether consumers would express a difference in preference for such a short range of stimuli variation. For the geometric variations of Table 5, the range of predicted sensory viscosity is more appropriate. The uncoded geometric concentrations and their corresponding perceived sensory viscosity can be linearly related to the coded levels by logarithmic transformations. For cocoa concentrations, as Steven’s Law exponent was 0.78, the geometric factor would be expected to be lower than for gum concentration (n = 0.37). Preliminary tests seemed to indicate that some consumers preferred an ever increasing level of cocoa. Because of this we wanted to cover a wide range of concentrations and chose a geometric factor of 2. Table 6 shows the nine points of the experimental design. It should be noted that magnitude estimation tests for gum concentration were done at constant cocoa concentration. When both are varied simultaneously for interactions might affect predicted sensory RSM, estimation tests response. In spite of this, magnitude proved a useful guide in choosing RSM levels.
Consumers
and sensory
than oral viscosity. Thus, for RSM evaluations, visual instead of oral viscosity was assessed. Sample presentation was the same as in Experiment 1. To avoid saturation, four sessions were used to evaluate the nine samples. In each session, Sample 1 from Table 6 was evaluated together with two other samples chosen at random among the eight remaining. In this way the center point was repeated four times by each consumer. In each session the three samples were presented in a balanced order to avoid position effect. Sessions were held at least three days apart. Genstat (1993) statistical package was used to perform analysis of variance (ANOVA) and multiple regression. p < 0.05 was used for significance.
Results A split-plot ANOVA following results:
on the preference
scores gave the
(a) There
were significant differences between samples for all attributes. Average preference scores over all consumers are presented in Table 7. (b) For all attributes children’s scores were significantly higher than adults. Average scores over all samples for children and adults were, respectively: 6.6 and 5.6 for appearance, 6.7 and 6.1 for texture, 6.6 and 5.9 for flavor and 6.8 and 5.9 for overall preference. Moskowitz (1994) also reported that children use higher numbers than do adults. Age x Sample interactions were significant for Cc) appearance and texture. For appearance it was due to adults giving sample five a very low score in relation to children. This sample had high cocoa and low gums (Table 6), producing an effect of partially dissolved and precipitated cocoa. Children did not seem to mind this, but adults did. As regards texture interactions, the most preferred samples were nine for children and six and one for adults, indicating that children prefer less viscous samples. Least preferred were eight for children and seven for adults. In evaluating visual viscosity it is possible that children were guided by the low color intensity of Sample 8.
tests
Sensory assessments were performed by a panel of 60 consumers, divided into 30 1 I-12-year-old children and 30 18-22-year-old young adults. Consumers evaluated samples using the hedonic scale shown in Fig. 1. A halo effect between oral viscosity and flavor was suggested in Experiment 1, and in Experiment 2 visual viscosity results were similar and with less error
TABLE 5. Coded and Uncoded Gum Concentrations in Reconstituted Powdered Chocolate Milk Assuming Arithmetic Geometric Concentration Variations, and Expected Sensory Viscosity Using Stevens Law with n = 0.37
Coded level -2 -1 0
1 2
Uncoded
Arithmetic level (%)
0.00 0.02 0.04
0.06 0.08
“Geometric factor for gum concentration
= 2.5.
variation Sensory
viscosity
77
100 116 129
Uncoded
Geometric level (%)
0.0064 0.016
0.04 0.10 0.25
variation0 Sensory
viscosity 51 71
100 140 197
and
Sensory Optimization of a Powdered Chocolate Milk TABLE 6. Coded and Uncoded Concentration in Reconstituted Used for RSM Optimization
Levels of Gum and Cocoa Powdered Chocolate Milk
Coded
Sample 1 2 3 4 5 6 7 8 9
significance p < 0.05. The only model which did not meet these criteria was appearance for adults. The texture and overall preference models for children did not present a quadratic gum term, leading to an optimum predicted gum concentration = 0. As this goes beyond the experimental range, optimum gum concentration for these attributes was taken = -2 (see Table 6). The regression models predicted optimum preferences between 7.6 and 7.9 for children and 7.0 for adults, as seen in the last row of Tables 7 and 8. To visualize the optimum formulation, contour plots for children corresponding to a preference score of 7.5 for all attributes are shown in Fig. 4. The corresponding plots for adults with a preference score of 7.0 are in Fig. 5. There is no unique formulation which is optimum for both age groups and all attributes. In choosing a formulation a compromise must be taken considering factors such as:
uncaded
Cocoa
Gum 0 -1 1 1 -1 0 2 0 -2
0 -1
Gum (%) 0.04 0.016
-1
0.10
1 1 2 0 -2 0
0.10 0.016 0.04 0.25 0.04 0.0064
Cocoa (%) 1.5 0.75 0.75 3.0 3.0 6.0 1.5 0.38 1.5
Due to the different scores and interaction effects mentioned above, stepwise multiple regression was performed separately on children and adults. Results are given in Tables 8 and 9. To evaluate the adequacy of the models the criteria proposed by Joglekar and May (1987) (cited by Malcolmson et al., 1993) were used: R2 > 0.80, coefficient of variation < 10% and model TABLE
7. Average
Preference
Sample
Hedonic
Concentrations’ Gum Cocoa
___ 1 2 3 4 5 6 7 8 9 “For uncoded
Scores on a l-9
0 -1 1 1 -1 0 2 0 -2
0 -1 -1 1 1 2 0 -2 0
2 19
l l
Scale for Response Appearance
6.6 5.5 5.6 7.1 6.1 6.6 5.3 3.7 6.6
what age group the product is primarily targeted to; whether appearance can be modified without affecting other attributes, for example by the use of a coloring; Surface Methodology Texture
Samples Flavor
6.9 6.0 6.0 7.0 6.6 6.8 5.2 4.9 6.8
Overall
7.1 5.8 5.3 6.9 7.0 4.7 5.6 3.8 7.0
7.0 6.2 5.5 7.1 6.9 5.7 5.6 4.1 7.2
levels see Table 6.
TABLE 8. Children’s Regression Equation Coefficients Calf standard Error, R*, F Probability of the Regression (F-prob), Coefficient of Variation (CV%), Optimum Gum and Cocoa Concentrations ([GUM] and [COCOA]) and Preference Score of the Optimum on a 1 to 9 Hedonic Scale Appearance
Texture
Flavor
Overall
:; (GUM) b2 (COCOA)
7.05f0.16 -0.27f0.11 0.76f0.11
6.95*0.13 -0.27*0.11 0.50 f 0.11
7.42+0.15 -0.29 h 0.09 0.17f0.09
7.26f0.10 -0.26 f 0.09 0.37 f 0.09
b,, b 22
-0.18 f 0.08 ~0.3OztO.08
(b) -0.24 f 0.08
-0.15f0.07 -0.71 f0.07
-0.43
blz -___
(b)
(b)
0.92 co.01 5.5
0.83 co.01 5.5
R2 F-PROB CV% [GUM]
coded uncoded (%) [COCOA]coded uncoded (%) Preference
0.75 0.02 1.27 3.62 7.6
-2.00 0.0064 1.03 3.06 7.8
(a) Preference=bo+blxl+b2x~+b~~~,2+b2~x22+b,~x~x2wherex~=[gum]andx:,=[cocoa]. the stepwise multiple regression.
(b)
(b) f 0.06 (b)
0.94 < 0.01 5.0
0.91 < 0.01 4.3
-0.97 0.016 0.12 1.63 7.6
--2.00 0.0064 0.42 2.01 7.9
(b) Th ese terms were not significant
in
220
G. Hough et al.
TABLE 9. Adults’ Regression Equation Coefficients @) f Standard Error, r2, f Probability of the Regression (F-prob), Coefficient of Variation (CV%), Optimum Gum and Cocoa Concentrations ([Gum] and [COCOA]) and Preference Score of the Optimum on a 1 to 9 Hedonic Scale Appearance 6.04 f 0.27
::, (GUM)
(b) 0.57 * 0.22 (b) -0.41 zkO.16 (b)
b2 (COCOA) brr bz bra R2 F-PROB CV%
Texture
Flavor
Overall
6.74*0.19 -0.21*0.12 0.43 l 0.12 -0.39 zk0.09 -0.26hO.09
6.83 f 0.21 +29ko.13 0.60 =k0.13 -0.25*0.10 -0.71+0.10
6.771k0.16 -0.34*0.10 0.58kO.10 -0.27ztO.08 -0.59 f 0.08
(b)
(b)
(b)
0.84 co.01 6.8
0.92 LO.01 7.7
0.94 < 0.01 5.9
0.60 0.02 13.8
[GUM] coded uncoded (%) [COCOA] coded uncoded (%) Preference
(c)
-0.40 0.028 0.82 2.65 7.0
(c) (c)
(a) Preference=bo+blxl+b2xl+blrxr the stepwise multiple regression.
-0.58 0.024 0.42 2.01 7.0
-0.63 0.022 0.49 2.11 7.0
2 + b22x22+ b12xIx2 where x, = [gum] and x2 = [cocoa]. (b) Th ese terms were not significant (c) Preference for appearance for adults did not show an adequate fit.
in
Overall -1 -2
0
-1
J -2
-1
Codedgum concentration
Coded gum concentration FIG. 4. Children’s contour butes, each one constructed 9 hedonic scale.
l
the
influence
product
plots for different preference attrifor a 7.5 preference score on a 1 to
each
attribute
has
on
the
overall
FIG. 5. Adults’ contour plots for different preference attributes, each one constructed for a 7.0 preference score on a 1 to 9 hedonic scale
preferences. tool
acceptability.
(b) The CONCLUSIONS
This
for conducting
product
both
use of Steven’s
vs cocoa concentration
To reach
be taken
whose
this objective
results
a number
can be summarized
in the milk
of steps had to as follows:
It was shown
that
1 l-year-old
age a self-administered measuring appearance,
children
verbal-numeric texture, flavor
can manscale for and overall
the
sensory
use of a common optimization
and young
Law
exponent
of the
adults. as a guide
in
levels for RSM methodology exponents for chocolate flavor and sensory
viscosity
vs gum
concentration were 0.78 and 0.37, respectively. This led to geometric variations in RSM levels instead of the traditional arithmetical variations.
Cc)Ideal 5.0%
(a)
allowed
in children
selecting adequate was discussed. The
The overall objective of the present work is expressed title: sensory optimization of a powdered chocolate formula.
0
level of sugar concentration was found to be for adults and 6.0% for children. A 5.0%
concentration
was chosen
mainly
ier product and because sugar consumers if they want to.
to avoid can
a bulk-
be added
by
Sensory Optimization of a Powdered Chocolate Milk
(4
The final optimization stage was using RSM. Children scored samples higher than adults. There were also Age x Sample interactions for appearance and texture preferences. There was no formulation which satisfied both age unique groups and all attributes.
221
Kimmel, S. A., Sigman-Grant, M. and Guinard, J. X. (1994) Sensory testing with young children. Food Technology 48(3), 92-99.
Kroll, B. J. (1990) Evaluating rating scales for sensory testing with children. Food Technol. 44( 1), 78-86. Lawless, H. T. and Malone, G. J. (1986) The discriminative efficiency of common scaling methods. Journal of Semosy Studies 1, 85-98.
ACKNOWLEDGEMENTS We wish to thank Sancor Coop. Ltd. (Sunchales, Santa Fe, Argentina) for their financial assistance in the development of this project.
REFERENCES Abdullah, A., Manundo, T. M. M., Resurrection, A. V. A. and Beuchat, L. R. (1993) Descriptive sensory profiling for optimizing the formula of peanut milk-based liquid coffee whitener. J. Food Science 58, 120-123. Calvirio, A. M. (1982) Evaluation de la consistencia aparente de la gelatina. Rev. Agroquim. Tecnol. Aliment. 22, 51 l-521. Christensen, C. M. and Casper, L. M. (1987) Oral and nonoral perception of solution viscosity. 3. Food Science 52, 445-447. Codigo Alimentario Argentino. (1992) Article 562. De la Canal y Asociados, Buenos Aires, Argentina. Conner, M. T., Land, D. G. and Booth, D. A. (1987) Effect of stimulus range on judgements of sweetness intensity in a lime drink. British Journal of Psychology 78, 357-364. Fishken, D. (1988) Marketing and cost factors in product optimization. Food Technol. 42( 1 I), 1388140. Gacula Jr., M. C. (1993) Design and Analysis of Sensov Optimization. Food and Nutrition Press, Connecticut, USA, pp. 143-153. Genstat 5. (1993) Release 3 Reference Manual. Genstat 5 Committee, Rothamstead Experimental Station, Harpenden, U.K. Giovanni, M. (1983) Response surface methodology and product optimization. Food Technol. 37( 11), 41-46. Henika, R. G. (1982) Use of response-surface methodology in sensory evaluation. Food Technol. 36( 11), 96100.
Malcolmson, L. J., Matsuo, R. R. and Balshaw, R. (1993) Textural optimization of spaghetti using response surface methodology: effects of drying temperature and durum protein level. Cereal Chemistry 70, 417-423. Matulis, R. J., Mckeity F. K., Sutherland J. W. and Brewen, M. S. (1995) Sensory characteristics of frankfurters as affected by fat, salt, and pH. Food Science 60, 42247. Meilgaard, M., Civille, G. V. and Carr, B. T. (1991) Sensory Evaluation Techniques. CRC Press, Florida, USA, pp. 40, 298-303. Mora-escobedo, R., Paredes-lopez, 0. and Dominguez, J. Optimization of a germination procedure by response surface methodology. Lebensm.- Wiss.u.- Technol. 24, 518-522. Mori, E. E. M., Shirose, I. and Junqueira, M. C. (1984) 0 metodo de estimativa de grandeza: revisao e aplicacao. Boletin ITAL 21, 291-323. Moskowitz, H. R. (1977) Magnitude estimation: notes on what, how, when, and why to use it. 3. of Food Quality 3, 195-227. Moskowitz, H. R. (1985). New Directions for Product Testing and Sensory Analysis of Foods, Food and Nutrition Press, Connecticut, USA, pp. 148-153. Moskowitz, H. R. (1994). Food Concepts and Products: &st in Time Development. Food and Nutrition Press, Connecticut, USA, pp. 296299, 321. Oude Ophius, P. A. M. (1994) Sensory evaluation of ‘free conditions of range’ and regular meat under different experience and awareness. Food Quality and Preference 5, 173178.
Schantz, R. and Bowers, J. (1993) Response surface of sensory characteristics for reduced sodium chloride and phosphate salts in emulsified turkey sausages. 3. Sensory Studies 8, 283-300.
Stevens, S. S. and Guirao, M. (1964) Scaling of apparent viscosity. Science 144, 1157-l 158. Stone, H. and Sidel, J. L. (1985) Sensory Evaluation Practices. Academic Press, New York, USA, pp. 76-80. Tressler, D. K. and Sultan, W. J. (1975) Food Products Formulary, Volume 2: Cereals, Baked Goods, Dairy and Ea Products. Avi Publishing Company, Connecticut, USA, pp. 27&272. Zamora, M. C. (1995) Relationship between sensory viscosity and apparent viscosity of corn starch pastes. 3. Texture Studies 26, 2 17-230.
and Prcfcmce
Vol. 8, No. 3, pp. 213-221, 1997 0 1997 Else&r Science Ltd All rights reserved. Printed in Great Britain
PII:SO950-3293(96)00051-l
ELSEVIER
0950-3293/97 917.00+.00
SENSORYOPTlMlZATlONOFAPOWDEREDCHOCOLATEMlLK FORMULA Guillermo Hough,* Ricardo Sbnchez, Teresa Barbieri & Edgardo Martinez Instituto Superior Experimental de Tecnologia Alimentaria, 6500 Nueve de Julio, Buenos Aires, Argentina (Accepted
1 November
When optimizing a formulation, a common goal is to maximize sensory acceptability. PCM would mainly be targeted to children and adolescents. There are a limited number of studies reporting the use of acceptability scales
ABSTRACT Four experiments were carried out to optimize the formulation of a powdered chocolate milk. The jrst of these compared facial vs. verbal-numeric self-administered scales when measuring sensory acceptability of di$erent attributes among il-year-old children. Both scales were equivalent. In a second experiment, magnitude estimation was performed to analyze sensory viscosity as a function of gum concentration, and chocolate flavor as a function of cocoa concentration. Steven’s Law exponents were: 0.37for uiscosity and 0.78for chocolateJlavor. Third, by using a minimum bias method, the optimum level of sugar concentration resulted in 5.0% for adults and 6.0% for children. In the fourth experiment
with children. The g-point hedonic scale is a widely used tool (Stone and Sidel, 1985) originally developed for adults and later adapted to children (Kroll, 1990). Facial scales have been used with children, although Stone and Side1 (1985) point out difficulties in their use. Kimmel et al. (1994) successfully used a combined facial and verbal scale with 8-lo-year-old children guided by an interviewer. For variations of sweetness in an orange drink Kroll (1990) showed that 8-lo-year-old children could use a self-administered g-point hedonic scale for overall acceptability. Moskowitz (1985, 1994) presents case studies where children use O-100 anchored scales guided by an interviewer, and with younger children use a ‘Snoopy’ facial scale. There are no studies on children using selfadministered scales to measure acceptability of different attributes like appearance, texture, flavor and overall acceptability.
gum and cocoa concentration were optimized using response surface methodology. Based on Steven’s Law exponents, levels were varied geometrically. Contour plots for appearance, texture, flavor and overall preference are presented for children and adults. No unique formula would satisfy both age groups for all attributes. 0 1997 Elsevier Science Ltd
In optimizing the PCM formulation, quantities of several ingredients can be modified to obtain maximum sensory acceptability. Response surface methodology (RSM) is an effective statistical method to investigate sensory characteristics when a number of ingredients are to be tested simultaneously (Henika, 1982). For PCM the objective of RSM is to predict optimal sensory acceptability by applying a formulation design where concentrations are varied according to a predefined
INTRODUCTION
Chocolate milk is a widely consumed most common modes of consumption
1996)
dairy product. The are adding a cocoa
pattern. Concentration levels do not usually affect sensory perception linearly, Steven’s Law (Moskowitz, 1977) is used to relate sensory perception to changes in a physical magnitude, in our case, concentration:
powder formulation to milk or buying the chocolate milk ready to drink. Powdered chocolate milk (PCM) that only needs water for reconstitution has interesting applications as a convenience beverage for the home and outdoor use (picnics and camping). PCM is marketed, e.g. by Cadbury’s in the United Kingdom (Premier Brands, Birmingham) or by DOS Alamos in Chile (Panguipulli, Valdivia), yet formulations have not been published and neither are there published reports on optimization of liquid chocolate milk formulations.
Sensory = k.(Concentration)”
Linear variations in concentration, normally used in RSM, could lead to exaggerated variations in sensory perception if n > 1 or small sensory variations (perhaps not significantly different) if n < 1. Thus to conduct sensory optimization by RSM, previous knowledge of Steven’s Law exponent can be of help in choosing concentration levels.
Research fellow of the Comision de Investigaciones Cientificas de la Provincia de Buenos Aires. 213
214 G. Houghet al. The objectives
(4 (b) (4
of the present work were:
one to include vanilla flavoring. By informal tasting among colleagues an initial formula (IF) was made up which had acceptable levels of color, texture, chocolate flavor and sweetness. A group of 12 1 l-year-old children also tried the IF together with two commercial fluid chocolate milks: 7 of them ranked the IF first and 5 ranked it second. The IF, the starting point for RSM optimization, is shown in Table 1. PCM was reconstituted so as to obtain 8.14% non-fat dairy solids in the reconstituted chocolate milk. When the IF was modified, e g. to increase cocoa concentration, the PCM composition and water for reconstitution were recalculated to maintain always the 8.14% non-fat dairy solids in the reconstituted PCM. The reconstitution was done by placing the powder over the water and
test different hedonic scales to be used with 1 lyear-old children, conduct magnitude estimation tests to estimate Steven’s Law exponent for selected PCM ingredients, and use RSM methodology to optimize PCM formulation based on sensory acceptability.
As results from one experiment influence the design of the following, each experiment, with results included, is presented sequentially.
EXPERIMENTl:HEDONIC SCALETO BE USED CHILDREN
WITH
1. Initial Powdered and Reconstituted Milk Formulation
TABLE
Initial formula
Non-fat dairy solids Dairy fat Cocoa
A major dairy company provided us with formulations for fluid chocolate milk and PCM. A fluid chocolate milk formula was also obtained from relevant literature (Tressler and Sultan, 1975). The major variations were in the type and quantity of stabilizing gums, and in the quantity of cocoa. The literature formula was the only
Reconstituted (% w/w)
Powdered (% w/w)
Comwnent
Sucrose
Glucose Xanthan gum Locust bean gum Water
Chocolate
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
CHOCOLATE MILK PREFEREtxE
NAME: -
You
. .
..I..........................
will
- Considering following
receive
3 glasses
everything, scale:
with
please
chocolate score
each
milk sample
using
the
9 - Super good
8 - Really good 7 - Good 6 - Just a little good 5 - Maybe good or maybe bad 4 - Just a little bad 3 - Bad 2 - Really bad 1 - Super bad
II
SAMPLE
I
II
815
I
SCORE
11
FIG. 1. Score sheet used by assessorsto measure overall preference for different samples of chocolate milk using a verbal numeric scale.
215
Sensory Optimization of a Powdered Chocolate Milk
CHOCOLATE MILK PREFERENCE
NAME:................................ -
YOU will
receive
- Considering
3 glasses
everything,
with
please
chocolate mark
milk
the appropriate
face.
FIG. 2. Score sheet used by assessors to measure overall preference for different samples of chocolate milk using a facial scale.
mixing
with an electric
tion was between
hand mixer for 30 sec. Prepara-
flavor evaluations. The
Sensory tests and experimental design
facial
re-scaled g-point
The
two types of scales that were tested are shown in
Figs 1 and 2. The chocolate IF (see Table
l),
and
(Nestle
Nesquik
Argentina) Figure dure.
milks used for the test were:
IF with
prepared
1.6%
according
3 shows a diagram
Sixty-four
chocolate scale first, days. Then
children,
participated
also performed verbal-numeric
duplicate
Group
duplicate
they received
Buenos
Aires,
to packet instructions.
into two groups.
performing
S.A.,
of the experimental
1 l-year-old
milk regularly,
were divided
sugars and 1% cocoa,
Argentina
who
measures.
proce-
repeated between
were available
7 points
so these
results
by 9/7 to compare
if
measures
model. variables,
subjects
Lawless and Malone
Groups and
variables.
and
milks
were
with
scale. The data were analyzed
subjects
were within
scales
and
the
using a were
duplicates
For similar
designs
( 1986) or Oude Ophius
( 1994).
see
Results The
(ANOVA)
on different scale and
Group 2 received the
Children received instructions on the attributes and scale to be used and were then conducted to individual booths. They received 50 ml of each of the three samples in 80 ml glasses coded with 3-digit random numbers, and overall preference.
and crackers
consumed
scale first and then the facial scale.
they were asked to evaluate
hedonic
1 used the facial
measures
scale had
by multiplying
in the test. They
the verbal-numeric
Water
wanted.
20 and 30 min before tasting.
The three
samples were removed and fresh ones, with different codes, were presented for appearance, oral texture and
main
numeric,
result showed
obtained
by
analysis
that both scales, facial
were equivalent
of
variance
and verbal-
for all attributes
(p > 0.20).
Average scores for each scale are in Table 2. It can be observed in Table 2 that the children discriminate
significantly
among
the samples.
did
As expec-
ted, preference for the low sugar-low cocoa sample was significantly lower in texture, flavor and overall preference. The IF had high preference scores showing it was a good starting point for RSM. Differences in texture between the IF and the low sugar-low cocoa were not high as the gum concentrations were kept equal, yet preference
scores were very different
216
G. Hough et al.
rFacia1 first (duplicate)
I
- Group 1 (32 children)
64
L
Verbal-Numeric (duplicate)
second
7
Verbal-Numeric (duplicate)
first
children
. Group 2 ( 32 children
I ) L
FIG. 3. Experimental
procedure
used to compare
Facial second (duplicate)
facial scales with verbal-numeric
scales in 1 l-year-old
TABLE 2. Average
Numeric
Hedonic
Chocolate
Preference Scores (1 = super bad, 9 = super good) for the Three Chocolate Scales with 1 l-year-old Children
milks
Initial formulation Low sugar-low cocoa Nesquik ‘*‘JDifferent
orally,
8.5 6.4 5.9
the sample This
texture
8.4” 6.0b 5.8b
i.e. and halo
visually
the
consequently led
instead
significant
8.0 3.4 7.6
differences
disliked rated
the
(4
of
in
the
measuring rest
of the
8.3 2.1 7.9
8.4 3.2 7.5
8.4” 2.6b 7.7”
chocolate
flavor
concentrations
0.67,
1.00,
1.50,
(b) viscosity (50%
the low sugar-low
cocoa
scale: 2.1 vs 3.1 respectively
This might indicate the products
the
following
2.25
and
3.37%
PCM:
(geometric
0.056,
PCM: 0.078,
gum concentrations locust
0.010, 0.11,
bean
gum)
0.015, 0.020, 0.15%
in
0.029,
(geometric
factor 21 1.4).
sample
lower scores with the facial scale than the ver-
bal-numeric
using
gum+ 50%
0.04,
but
8.0” 3.6b 7.6’
in the reconstituted
using the following
xanthan
The only significant interaction was Sample x Scale for flavor. The IF and Nesquik received equivalent scores scales,
8.1 3.7 7.7
factor 11 1.5).
the reconstituted
for both
7.9 3.5 7.4
intensity
cocoa
experiments.
received
Overall preference Facial Verbal Average
(Jo< 0.05).
it low in texture
us to consider
of orally
flavor
Milks Used to Test Facial and Verbal-
Flavor Facial Verbal Average
8.0” 3.2b 7.7”
et al., 1991) with flavor
children
effect
8.1 2.9 7.8
2). As texture was evaluated
(see Table
a halo effect (Meilgaard
is suggested, also.
8.3 5.6 5.7
letters in each column indicate
for this attribute
Texture Facial Verbal Average
Appearence Facial Verbal Average
children.
(see Table
2).
lower scores for the facial scale when
are low in preference.
Sensory
evaluations
were performed
by a 10 member
trained panel who had previous experience
in magnitude
estimation
presented
measurements.
Samples
were
in
random order in 80 ml glasses coded with 3-digit random numbers. For chocolate flavor the glasses were placed in a box and under red light to mask color dif-
EXPERIMENT ESTIMATION
2:MAGNITUDE
uated
due to different amounts of cocoa. Water and were used as neutralizers. Viscosity was evalvisually
and
orally.
For
the
visual
evaluation
assessors poured the milk from one glass to another observing flow and turbulence. Oral viscosity was eval-
Samples and sensory tests Fixed modulus magnitude 1977) were performed on:
ferences crackers
estimation
tests (Moskowitz,
uated by taking a generous mouthful of milk and manipulating it in the mouth. Chocolate flavor, visual and oral viscosity were evaluated separately on three
Sensory Optimization of a Powdered Chocolate Milk TABLE 3. Steven’s Law Exponents, 95% Confidence Intervals, and Percent Variance Explained by Regression of Magnitude Estimation Results
Exponent 95Ya con6dence
s--Y
Chocolate flavor Visual viscosity Oral viscosity
different Mori
days.
0.78 0.37 0.37
Results
were
lated by consumers who prefer very sweet products. Before introducing sugar concentration as an extra variable in RSM, the ideal level of this ingredient was measured.
YO variance erplained
interval
attribute
hO.12 * 0.04 f 0.08
98 98 92
evaluated
as detailed
Sensory method
by
et al. ( 1984).
Results Table
3 gives Steven’s
linear
regression
The relatively
Law exponents
large
confidence
estimated
from the
vs log(concentration)
of log(sensory)
interval
.
for the chocolate
flavor exponent is due to only five experimental points being used. The exponents for oral and visual viscosity published by other authors (see Table 4) are in the same range as our values for chocolate milk, even though the physical measurements for values in Table 4 were instrumental viscosity and not gum concentration as in our case. Zamora (1995) showed standard deviations for oral viscosity assessments were greater than for visual assessments. Our conclusions are similar (Table 3). No literature values were found to compare with the exponent for chocolate flavor intensity.
Introduction When deciding on which ingredient quantities to optimize, milk solids were kept constant to respect food regulation limitations (Codigo Alimentario Argentino 1992). When deriving IF (see Experiment 1), sugar was between 5 and 6% in the different formulations consulted. It is also an ingredient which can be manipufor Steven’s
Law
Exponents
Type of evaluation
Product
Exponent
Calvirio (1982)
Visual
Silicones
0.38
Christensen (1987)
Oral Visual
Na alginate Solutions
0.34 0.39
Oral Visual
Silicones
0.42 0.42
Oral Visual
Starch Pastes
Autborls
and Casper
Stevens and Guirao (1964) Zamora
( 1995)
Sugar concentrations used to measure the ideal level were: 1, 2, 4, 8 and 16% in the reconstituted PCM; with the rest of the ingredients as in the IF except water. A minimum bias procedure published by Conner et al. (1987) was used. Each sample was presented individually and rated on a non-structured 100 mm scale anchored ‘not at all sweet’ at the left end, ‘ideal sweetness’ in the middle and ‘extremely sweet’ at the right end. The first sample presented was 4% sugar concentration. After rating this the assessor handed back the glass and the rating slip. Panel leaders were instructed to choose the second sample so that it would be likely to be rated on the other side of the ideal mid-point regarding the first sample, but not at an extreme. Subsequent samples were to be selected for presentation in a way that would be likely to alternate responses on either side of the mid-point, while also balancing the responses around it such that the mean of the session of responses was close to the midpoint. For each assessor a linear regression of sensory scores vs log sugar concentration was calculated. Ideal sugar level was derived from the regression by calculating the concentration corresponding to the mid-point of the scale. The test was performed by 30 1 l-year-old children and by 30 adults between 18 and 40 years old, all of whom consumed chocolate milk at least once a month.
Results
EXPERIMENT3:IDEALSUGAR LEVEL
TABLE 4. Literature Values when Measuring Viscosity
2 17
0.40 0.41
Mean f standard deviation ideal sugar levels were: 6.0 f 2.2 for children, 5.0 f 1.5 for adults, and 5.5 f 1.9 grouping all assessors together. A Student’s ttest indicated that children had a significantly higher ideal sugar level than adults. Sugar concentration was kept at 5% (IF, Table 1) and not included as an RSM independent variable due to: (a) increase in sugar concentration would mean a bulkier PCM, (b) consumers can add more sugar if they please, (c) RSM is simplified if the number of independent variables are kept to a minimum and (d) sugar concentration in the IF (Table 1) was close to the mean ideal values.
EXPERIMENT4:RESPONSE SURFACEMETHODOLOGY Choice of levels Having discarded milk solids and sugar, the two variables to optimize by RSM were cocoa and gum concentration. A two-factor central composite design was used (Gacula, 1993).
218
G. Hough et al.
As regards the levels, success in defining the optimum product depends on the selection of appropriate factor levels within given limitations (Giovanni, 1983). Most authors have used arithmetic variations of independent variables for RSM optimization (Henika, 1982; Fishken, 1988; Schantz and Bowers, 1993; Matulis et al., 1995). Non-arithmetical variations have been used (Abdullah et al., 1993; Mora-Escobedo et al., 1991) but with no reasons given. Table 5 shows coded and uncoded concentrations and predicted sensory viscosity using Steven’s Law, assuming arithmetical and geometric variations in gum concentration. When changing gum concentration from 0.04 to 0.06%, changes in predicted sensory viscosity only changed from 100 to 116. It is doubtful whether consumers would express a difference in preference for such a short range of stimuli variation. For the geometric variations of Table 5, the range of predicted sensory viscosity is more appropriate. The uncoded geometric concentrations and their corresponding perceived sensory viscosity can be linearly related to the coded levels by logarithmic transformations. For cocoa concentrations, as Steven’s Law exponent was 0.78, the geometric factor would be expected to be lower than for gum concentration (n = 0.37). Preliminary tests seemed to indicate that some consumers preferred an ever increasing level of cocoa. Because of this we wanted to cover a wide range of concentrations and chose a geometric factor of 2. Table 6 shows the nine points of the experimental design. It should be noted that magnitude estimation tests for gum concentration were done at constant cocoa concentration. When both are varied simultaneously for interactions might affect predicted sensory RSM, estimation tests response. In spite of this, magnitude proved a useful guide in choosing RSM levels.
Consumers
and sensory
than oral viscosity. Thus, for RSM evaluations, visual instead of oral viscosity was assessed. Sample presentation was the same as in Experiment 1. To avoid saturation, four sessions were used to evaluate the nine samples. In each session, Sample 1 from Table 6 was evaluated together with two other samples chosen at random among the eight remaining. In this way the center point was repeated four times by each consumer. In each session the three samples were presented in a balanced order to avoid position effect. Sessions were held at least three days apart. Genstat (1993) statistical package was used to perform analysis of variance (ANOVA) and multiple regression. p < 0.05 was used for significance.
Results A split-plot ANOVA following results:
on the preference
scores gave the
(a) There
were significant differences between samples for all attributes. Average preference scores over all consumers are presented in Table 7. (b) For all attributes children’s scores were significantly higher than adults. Average scores over all samples for children and adults were, respectively: 6.6 and 5.6 for appearance, 6.7 and 6.1 for texture, 6.6 and 5.9 for flavor and 6.8 and 5.9 for overall preference. Moskowitz (1994) also reported that children use higher numbers than do adults. Age x Sample interactions were significant for Cc) appearance and texture. For appearance it was due to adults giving sample five a very low score in relation to children. This sample had high cocoa and low gums (Table 6), producing an effect of partially dissolved and precipitated cocoa. Children did not seem to mind this, but adults did. As regards texture interactions, the most preferred samples were nine for children and six and one for adults, indicating that children prefer less viscous samples. Least preferred were eight for children and seven for adults. In evaluating visual viscosity it is possible that children were guided by the low color intensity of Sample 8.
tests
Sensory assessments were performed by a panel of 60 consumers, divided into 30 1 I-12-year-old children and 30 18-22-year-old young adults. Consumers evaluated samples using the hedonic scale shown in Fig. 1. A halo effect between oral viscosity and flavor was suggested in Experiment 1, and in Experiment 2 visual viscosity results were similar and with less error
TABLE 5. Coded and Uncoded Gum Concentrations in Reconstituted Powdered Chocolate Milk Assuming Arithmetic Geometric Concentration Variations, and Expected Sensory Viscosity Using Stevens Law with n = 0.37
Coded level -2 -1 0
1 2
Uncoded
Arithmetic level (%)
0.00 0.02 0.04
0.06 0.08
“Geometric factor for gum concentration
= 2.5.
variation Sensory
viscosity
77
100 116 129
Uncoded
Geometric level (%)
0.0064 0.016
0.04 0.10 0.25
variation0 Sensory
viscosity 51 71
100 140 197
and
Sensory Optimization of a Powdered Chocolate Milk TABLE 6. Coded and Uncoded Concentration in Reconstituted Used for RSM Optimization
Levels of Gum and Cocoa Powdered Chocolate Milk
Coded
Sample 1 2 3 4 5 6 7 8 9
significance p < 0.05. The only model which did not meet these criteria was appearance for adults. The texture and overall preference models for children did not present a quadratic gum term, leading to an optimum predicted gum concentration = 0. As this goes beyond the experimental range, optimum gum concentration for these attributes was taken = -2 (see Table 6). The regression models predicted optimum preferences between 7.6 and 7.9 for children and 7.0 for adults, as seen in the last row of Tables 7 and 8. To visualize the optimum formulation, contour plots for children corresponding to a preference score of 7.5 for all attributes are shown in Fig. 4. The corresponding plots for adults with a preference score of 7.0 are in Fig. 5. There is no unique formulation which is optimum for both age groups and all attributes. In choosing a formulation a compromise must be taken considering factors such as:
uncaded
Cocoa
Gum 0 -1 1 1 -1 0 2 0 -2
0 -1
Gum (%) 0.04 0.016
-1
0.10
1 1 2 0 -2 0
0.10 0.016 0.04 0.25 0.04 0.0064
Cocoa (%) 1.5 0.75 0.75 3.0 3.0 6.0 1.5 0.38 1.5
Due to the different scores and interaction effects mentioned above, stepwise multiple regression was performed separately on children and adults. Results are given in Tables 8 and 9. To evaluate the adequacy of the models the criteria proposed by Joglekar and May (1987) (cited by Malcolmson et al., 1993) were used: R2 > 0.80, coefficient of variation < 10% and model TABLE
7. Average
Preference
Sample
Hedonic
Concentrations’ Gum Cocoa
___ 1 2 3 4 5 6 7 8 9 “For uncoded
Scores on a l-9
0 -1 1 1 -1 0 2 0 -2
0 -1 -1 1 1 2 0 -2 0
2 19
l l
Scale for Response Appearance
6.6 5.5 5.6 7.1 6.1 6.6 5.3 3.7 6.6
what age group the product is primarily targeted to; whether appearance can be modified without affecting other attributes, for example by the use of a coloring; Surface Methodology Texture
Samples Flavor
6.9 6.0 6.0 7.0 6.6 6.8 5.2 4.9 6.8
Overall
7.1 5.8 5.3 6.9 7.0 4.7 5.6 3.8 7.0
7.0 6.2 5.5 7.1 6.9 5.7 5.6 4.1 7.2
levels see Table 6.
TABLE 8. Children’s Regression Equation Coefficients Calf standard Error, R*, F Probability of the Regression (F-prob), Coefficient of Variation (CV%), Optimum Gum and Cocoa Concentrations ([GUM] and [COCOA]) and Preference Score of the Optimum on a 1 to 9 Hedonic Scale Appearance
Texture
Flavor
Overall
:; (GUM) b2 (COCOA)
7.05f0.16 -0.27f0.11 0.76f0.11
6.95*0.13 -0.27*0.11 0.50 f 0.11
7.42+0.15 -0.29 h 0.09 0.17f0.09
7.26f0.10 -0.26 f 0.09 0.37 f 0.09
b,, b 22
-0.18 f 0.08 ~0.3OztO.08
(b) -0.24 f 0.08
-0.15f0.07 -0.71 f0.07
-0.43
blz -___
(b)
(b)
0.92 co.01 5.5
0.83 co.01 5.5
R2 F-PROB CV% [GUM]
coded uncoded (%) [COCOA]coded uncoded (%) Preference
0.75 0.02 1.27 3.62 7.6
-2.00 0.0064 1.03 3.06 7.8
(a) Preference=bo+blxl+b2x~+b~~~,2+b2~x22+b,~x~x2wherex~=[gum]andx:,=[cocoa]. the stepwise multiple regression.
(b)
(b) f 0.06 (b)
0.94 < 0.01 5.0
0.91 < 0.01 4.3
-0.97 0.016 0.12 1.63 7.6
--2.00 0.0064 0.42 2.01 7.9
(b) Th ese terms were not significant
in
220
G. Hough et al.
TABLE 9. Adults’ Regression Equation Coefficients @) f Standard Error, r2, f Probability of the Regression (F-prob), Coefficient of Variation (CV%), Optimum Gum and Cocoa Concentrations ([Gum] and [COCOA]) and Preference Score of the Optimum on a 1 to 9 Hedonic Scale Appearance 6.04 f 0.27
::, (GUM)
(b) 0.57 * 0.22 (b) -0.41 zkO.16 (b)
b2 (COCOA) brr bz bra R2 F-PROB CV%
Texture
Flavor
Overall
6.74*0.19 -0.21*0.12 0.43 l 0.12 -0.39 zk0.09 -0.26hO.09
6.83 f 0.21 +29ko.13 0.60 =k0.13 -0.25*0.10 -0.71+0.10
6.771k0.16 -0.34*0.10 0.58kO.10 -0.27ztO.08 -0.59 f 0.08
(b)
(b)
(b)
0.84 co.01 6.8
0.92 LO.01 7.7
0.94 < 0.01 5.9
0.60 0.02 13.8
[GUM] coded uncoded (%) [COCOA] coded uncoded (%) Preference
(c)
-0.40 0.028 0.82 2.65 7.0
(c) (c)
(a) Preference=bo+blxl+b2xl+blrxr the stepwise multiple regression.
-0.58 0.024 0.42 2.01 7.0
-0.63 0.022 0.49 2.11 7.0
2 + b22x22+ b12xIx2 where x, = [gum] and x2 = [cocoa]. (b) Th ese terms were not significant (c) Preference for appearance for adults did not show an adequate fit.
in
Overall -1 -2
0
-1
J -2
-1
Codedgum concentration
Coded gum concentration FIG. 4. Children’s contour butes, each one constructed 9 hedonic scale.
l
the
influence
product
plots for different preference attrifor a 7.5 preference score on a 1 to
each
attribute
has
on
the
overall
FIG. 5. Adults’ contour plots for different preference attributes, each one constructed for a 7.0 preference score on a 1 to 9 hedonic scale
preferences. tool
acceptability.
(b) The CONCLUSIONS
This
for conducting
product
both
use of Steven’s
vs cocoa concentration
To reach
be taken
whose
this objective
results
a number
can be summarized
in the milk
of steps had to as follows:
It was shown
that
1 l-year-old
age a self-administered measuring appearance,
children
verbal-numeric texture, flavor
can manscale for and overall
the
sensory
use of a common optimization
and young
Law
exponent
of the
adults. as a guide
in
levels for RSM methodology exponents for chocolate flavor and sensory
viscosity
vs gum
concentration were 0.78 and 0.37, respectively. This led to geometric variations in RSM levels instead of the traditional arithmetical variations.
Cc)Ideal 5.0%
(a)
allowed
in children
selecting adequate was discussed. The
The overall objective of the present work is expressed title: sensory optimization of a powdered chocolate formula.
0
level of sugar concentration was found to be for adults and 6.0% for children. A 5.0%
concentration
was chosen
mainly
ier product and because sugar consumers if they want to.
to avoid can
a bulk-
be added
by
Sensory Optimization of a Powdered Chocolate Milk
(4
The final optimization stage was using RSM. Children scored samples higher than adults. There were also Age x Sample interactions for appearance and texture preferences. There was no formulation which satisfied both age unique groups and all attributes.
221
Kimmel, S. A., Sigman-Grant, M. and Guinard, J. X. (1994) Sensory testing with young children. Food Technology 48(3), 92-99.
Kroll, B. J. (1990) Evaluating rating scales for sensory testing with children. Food Technol. 44( 1), 78-86. Lawless, H. T. and Malone, G. J. (1986) The discriminative efficiency of common scaling methods. Journal of Semosy Studies 1, 85-98.
ACKNOWLEDGEMENTS We wish to thank Sancor Coop. Ltd. (Sunchales, Santa Fe, Argentina) for their financial assistance in the development of this project.
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