This document summarizes a presentation on using sensory research and preference mapping to drive product innovation. Some key points: 1) Sensory research can be used throughout the innovation process from idea generation to product launch to understand consumer acceptance and drivers of liking. 2) Preference mapping is a statistical technique that can identify market segments with different preferences (clusters) and sensory attributes that drive liking (loadings plot). 3) Applying preference mapping to a set of commercial products revealed that preference was highest for products with floral, fruity, and sour attributes (e.g. Products C, F, and G) and lowest for the bitter Product E.

1. Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.com

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3. 11
Dudsadee Archakraisorn
Sensory Laboratory Manager
M.Sc. (Food Science)
Certificate Program of “Applied Sensory and Consumer Science” from UC Davis Extension (2012).
How this Leads to Fresh Ideas and Innovation
on 17 May 2013 in Bangkok, Thailand

4. 22
• How PD attain success in innovation with insights
from sensory research.
• What are the new trends in sensory research?
• What sensory research reveals and how this helps in
product development.

5. 33

6. 44
We test our recipe and
We can make it
Idea of “Healthy Pizza”
Initial Consumer validation
IDEAS
 We develop the recipe
on “HOW” to make it
We set up financial, and
cost/margin target
 We ask resources to
make it
INITIATION DEVELOPMENT
Let make many of Pizza
LAUNCH and SHIP
 Launch and
Follow up the launch
result
POST LAUNCH
EVALUATION
1st
S
H
I
P
IDEAGENERATION
IDEA
APPROVAL
GATE
PROJECT
APPROVAL
GATE
LAUNCH
APPROVAL
GATE
PROJECT
CLOSE
GATE
Stage 1 Stage 2 Stage 3 Stage 4
Gate 1 Gate 2 Gate 3 Gate 4
Innovation Process
Sensory Role
Modified from: http://www.prod-dev.com/stage-gate.php

7. 55
 Acceptability/Preference
 Home Use Test
 Driver of Liking
 Positioning in market
 Usage and Attitude
IDEAS
 Acceptability/Preference
 Just Right
 Discrimination
INITIATION DEVELOPMENT
 Discrimination
LAUNCH and SHIP POST LAUNCH
EVALUATION
1st
S
H
I
P
IDEAGENERATION
IDEA
APPROVAL
GATE
PROJECT
APPROVAL
GATE
LAUNCH
APPROVAL
GATE
PROJECT
CLOSE
GATE
Stage 1 Stage 2 Stage 3 Stage 4
What would we know in each step?
Sensory Role
Modified from: http://www.prod-dev.com/stage-gate.php

8. 66
Preference Mapping & Clustering

9. 77
Preference Mapping and Preference Clustering.
• To investigate market segmentation (preference clustering) and sensory drivers of
liking (internal preference mapping).
• Using multivariate methods of analysis (PCA – Principal Component Analysis).
• To find new directions (dimensions or principal components) in the
multidimensional space of observations that display most variation among
observations.
• To find loadings plots that approximate the correlation or covariance matrices in
that space.
New Trends
X1 X2 X3 … X100 Global CL1 CL2 CL3 fruity floral sour citric citrus oil cooked
Product A 5 6 4 … 2 5.29 5.53 2.55 5.68 8.91 8.90 1.81 4.02 4.58 2.49
Product B 5 5 1 … 6 5.10 5.20 2.64 5.51 9.53 9.48 1.65 4.00 4.57 2.52
Observations Product C 4 5 5 … 7 5.51 5.40 4.82 5.69 9.68 9.65 1.64 4.00 4.56 2.56
= Sample Product D 5 5 6 … 2 5.41 5.23 3.18 5.92 11.14 11.01 1.69 3.49 4.07 2.58
Product E 2 4 3 … 7 4.71 2.47 3.55 6.07 2.05 2.06 0.90 2.50 3.10 2.62
Product F 3 4 2 … 5 5.50 5.33 2.91 6.07 10.71 10.58 1.52 3.64 4.19 2.49
Active Variables = Liking score (N = 100) Supplement Variables; sensory dataSupplement; Global Liking, CL1, CL2, CL3

10. 88
1. First we calculate the average of each
variable (avg. liking score of 6 pdts for X1,
X2,…X100)
2. The mean-centering procedure corresponds
to moving the co-ordinate system.
4. The direction of PC1 is given by the cosine of
the angles αx1,αx2, andαx3. These values
indicate how the variables x1, x2, and x3 “load”
into PC1. Hence, they are called loadings.
New Trends
3.1 The first principal component is the line in X-
space that best approximates the data (least
squares). The line goes through the average point.
3.2 The second PC is represented by a line in X-
space orthogonal to first PC.
(Hasted, 2006)

11. 99
Preference Mapping and Preference Clustering: Key Terms of PCA
a. Eigenvalue reflects the quality of the projection from the N-dimensional initial table to a lower
number of dimensions.
b. Variability (%) or Contribution (%) of data onto each component/ dimension.
• Variability (%) indicates how well of projection quality on each factor.
c. Squared cosines: The greater the squared cosine, the greater the link with the corresponding axis.
d. Factor loading is a new score of individual observation on the principal component.
Principal Component Analysis:
Eigenvalues:
F1 F2 F3 F4 F5 F6
Eigenvalue 26.702 15.982 15.575 14.818 13.089 10.835
Variability (%) 27.528 16.476 16.056 15.276 13.494 11.170
Cumulative % 27.528 44.004 60.060 75.336 88.830 100.000
• The first two or three eigenvalues
will correspond to a high % of the
variance, ensuring us that the maps
based on the first two or three factors
are a good quality projection of the
initial multi-dimensional table.
New Trends
Squared cosines of the variables:
F1 F2 F3 F4 F5 F6
Global Liking 0.700 0.159 0.000 0.040 0.022 0.078
CL1 = 30 0.903 0.000 0.078 0.000 0.000 0.019
CL2 = 11 0.002 0.475 0.384 0.058 0.004 0.077
CL3 = 59 0.269 0.087 0.024 0.443 0.172 0.005
fruity 0.667 0.000 0.173 0.095 0.038 0.026
floral 0.688 0.000 0.165 0.087 0.036 0.024
sour 0.980 0.008 0.005 0.006 0.000 0.000
citric 0.783 0.000 0.087 0.110 0.002 0.018
citrus oil 0.769 0.002 0.096 0.107 0.006 0.021
cooked 0.448 0.062 0.049 0.074 0.299 0.068
watery 0.084 0.064 0.708 0.000 0.003 0.141
syrup 0.238 0.190 0.189 0.101 0.147 0.136
astringen 0.039 0.087 0.161 0.573 0.115 0.025
rancid 0.146 0.305 0.052 0.105 0.392 0.000
sweet 0.207 0.247 0.060 0.083 0.293 0.109
smooth 0.011 0.184 0.594 0.000 0.007 0.203
bitter 0.928 0.014 0.056 0.000 0.001 0.001
artificial 0.169 0.156 0.105 0.123 0.434 0.012
Pdt-A 0.083 0.588 0.001 0.058 0.249 0.021
Pdt-B 0.004 0.007 0.277 0.178 0.267 0.267
Pdt-C 0.019 0.074 0.013 0.092 0.148 0.654
Pdt-D 0.066 0.046 0.091 0.601 0.178 0.017
Pdt-E 0.928 0.014 0.056 0.000 0.001 0.001
Pdt-F 0.001 0.128 0.461 0.185 0.202 0.023
Pdt-G 0.065 0.309 0.267 0.052 0.123 0.184

12. 1010
Preference Mapping and Preference Clustering
• Step of conducting preference mapping;
a) Prepare commercial products.
b) Determine sensory descriptive analysis of these products.
c) Select a set of products (N = 6-12), of which the sensory profiles cover a range of
product category (low, medium, high intensity of studying attributes).
d) Conduct a consumer test (CLT, N = 100).
• Use the 9-point hedonic scale or any liking scale.
• Ask overall liking question first of all products.
• Use a randomized, balanced complete design.
• Put the U&A questions at the end of tasting the whole samples.
e) Collect the data results of liking score.
f) Run both of “cluster analysis” and “internal preference mapping”.
New Trends

13. 1111
How to read the map? (Hasted, 2006)
 The cosine of the angle between
the two arrows joining attributes
to the origin is approximating the
correlation between the attributes.
 Two arrows are in the same
direction indicate the attributes are
positively correlated.
 Two arrows in the opposite
direction indicate the attributes are
negatively correlated.
 The length of the arrow from the
origin to the unit circle indicates
how well the variation in that
attribute is being explained.
Interpretation:
• Preference is towards those products that have floral, fruity, sour (eg. Pdt C, Pdt G, and Pdt F), and
away from Pdt E, which was found bitter.
• Strongly “Positive” driver of liking are fruity, floral, sour.
• Strongly “Negative” driver of liking is bitter.
New Trends
Variables (axes F1 and F2: 44.00 %)
Pdt-G
Pdt-F
Pdt-E
Pdt-D
Pdt-C
Pdt-B
Pdt-A
artificial
bitter
smooth
sweet
rancid
astringen
syrup
waterycooked
citrus oil
citric
sour
floral fruity
CL3 = 59
CL2 = 11
CL1 = 30
Global Liking
-1
-0.75
-0.5
-0.25
0
0.25
0.5
0.75
1
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
F1 (27.53 %)
F2(16.48%)
Active variables Supplementary variables

14. 12
Variables Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-G
fruity 0.069 0.157 0.177 0.384 -0.899 0.323 -0.212
floral 0.072 0.156 0.180 0.376 -0.910 0.315 -0.190
sour 0.371 0.143 0.121 0.194 -0.959 -0.053 0.184
citric 0.328 0.307 0.311 -0.112 -0.922 0.019 0.069
citrus oil 0.335 0.323 0.319 -0.100 -0.915 0.010 0.028
cooked -0.296 -0.104 0.204 0.357 0.703 -0.304 -0.560
watery 0.158 0.216 0.072 -0.331 0.101 0.581 -0.796
syrup 0.398 0.437 0.440 -0.045 -0.537 -0.132 -0.562
astringen -0.077 -0.236 -0.236 0.986 -0.073 -0.236 -0.130
rancid 0.929 -0.228 -0.228 -0.228 -0.228 -0.228 0.209
sweet 0.051 -0.131 -0.161 -0.326 -0.413 0.064 0.917
smooth -0.298 0.195 0.446 -0.337 -0.350 0.694 -0.350
bitter -0.167 -0.167 -0.167 -0.167 1.000 -0.167 -0.167
artificial -0.831 0.248 0.248 0.248 0.248 0.248 -0.410
Variables
Global
Liking CL1 = 30 CL2 = 11 CL3 = 59
fruity 0.768 0.917 -0.238 -0.353
floral 0.779 0.926 -0.223 -0.362
sour 0.776 0.958 -0.051 -0.604
citric 0.704 0.939 -0.034 -0.740
citrus oil 0.685 0.936 -0.059 -0.755
cooked -0.616 -0.665 -0.020 0.281
watery -0.216 0.006 -0.612 0.006
syrup 0.223 0.625 -0.346 -0.794
astringen 0.098 0.054 -0.151 0.225
rancid 0.120 0.283 -0.191 -0.283
sweet 0.506 0.326 0.456 -0.041
smooth 0.379 0.385 -0.042 -0.066
bitter -0.864 -0.988 0.017 0.502
artificial -0.180 -0.274 0.044 0.248
12
Correlation matrix
(Pearsons (n)):
1) Liking vs Sensory data2) Liking vs Product
3) Product vs Sensory data
Variables Global Liking CL1 = 30 CL2 = 11 CL3 = 59
Pdt-A 0.002 0.267 -0.430 -0.314
Pdt-B -0.282 0.131 -0.389 -0.669
Pdt-C 0.331 0.212 0.587 -0.279
Pdt-D 0.181 0.144 -0.145 0.182
Pdt-E -0.864 -0.988 0.017 0.502
Pdt-F 0.316 0.185 -0.267 0.502
Pdt-G 0.316 0.049 0.627 0.076
• Select Pdt C for product
launch.
• Use the data in 2) to
draw the normalized liking
score of each cluster (see
the next slide).
• Gap of improvement for Pdt C would be increasing fruity, floral, sour, and reducing bitter.
New Trends

15. 1313
Normalized Liking Scores for each Cluster Segment
-1.00
-0.50
0.00
0.50
1.00
Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-G
Global Liking
CL1 = 30
CL2 = 11
CL3 = 59
• The chart as above shows “pattern” of liking among clusters.
• We will get through which product should be launched in which market.
• Product C well performed as a Global Liking.
• Summary of “Positive” and “Negative” driver of liking for each cluster.
Strongly Global = 100 CL1 = 30 CL2 = 11 CL3 = 59
Positive: fruity, floral, sour fruity, floral, sour, citric,
citrus oil
NA NA
Negative: bitter bitter NA citrus oil, syrup
New Trends

16. 1414
 Acceptability/Preference
 Home Use Test
 Driver of Liking
 Positioning in market
 Usage and Attitude
IDEAS
 Acceptability/Preference
 Just Right
 Discrimination
INITIATION DEVELOPMENT
 Discrimination
LAUNCH and SHIP POST LAUNCH
EVALUATION
1st
S
H
I
P
IDEAGENERATION
IDEA
APPROVAL
GATE
PROJECT
APPROVAL
GATE
LAUNCH
APPROVAL
GATE
PROJECT
CLOSE
GATE
Stage 1 Stage 2 Stage 3 Stage 4
What would we know in each step?
Sensory Role
Modified from: http://www.prod-dev.com/stage-gate.php

17. 1515
Nutrient profile
GO/
NO GO
Pilot Trial
Plant Trial
Qualification
Sensory
Evaluation
Product
Formulation
GO/
NO GO
Product Developer
Pilot Teams
Sensory Scientist
Innovation Teams
Process Engineer
Analytical Lab
Scientist
LAUNCH
No
No
Yes
Yes
Successful Innovation

18. 1616
Typical Project’s Success Criteria
For example,
• Overall acceptability of new prototype is parity with competitor’s
product.
• Product’s shelf-life as targeted.
• Taste improvement; with significant less BOTTOM 4.
Successful Innovation

19. 1717
1. Just-About-Right/ Penalty Analysis
2. Similarity Test

20. 1818
1. Just-About-Right / Penalty Analysis (ASTM, 2009)
• Use to determine the optimum levels of attributes in a product.
• With just-right scale, the intensity and hedonic judgments are combined to provide directional
information for product optimization.
Sample description:
312 – chicken broth containing 1% KCl/ AMP* (15:1)
430 – chicken broth containing 1% KCl/L-Arg*/AMP* (15:2:1)
151 – chicken broth containing 1% NaCl (CONTROL)
Sensory Role
*L-arginine and AMP (5’-adenosine
monophosphate) have a synergic effect not
only in inhibit undesirable bitterness taste, but
also enhancing saltiness taste in KCl solutions.

21. 1919
Sensory Role
Sample
312
Saltines
s liking S JAR B JAR
Overall
liking
Sample
312
Saltines
s liking S JAR B JAR
Overall
liking
1 6 1 3 3 26 6 2 3 2
2 7 2 1 7 27 2 1 3 2
3 4 3 1 3 28 6 2 3 2
4 6 2 2 3 29 2 1 3 2
5 4 3 3 3 30 3 1 2 2
6 5 1 2 5 31 4 2 1 4
7 2 1 3 2 32 2 3 2 2
8 2 3 3 2 33 6 1 1 5
9 4 3 3 3 34 2 1 3 2
10 6 2 1 6 35 4 2 2 3
11 3 3 3 3 36 6 1 1 5
12 4 1 2 3 37 6 1 2 3
13 3 3 3 3 38 2 3 2 3
14 4 1 3 3 39 4 1 1 1
15 4 1 2 3 40 3 1 3 4
16 1 3 3 1 41 3 3 1 3
17 6 2 2 6 42 2 3 3 1
18 4 2 3 2 43 2 1 1 4
19 2 2 3 2 44 4 1 3 4
20 2 3 2 3 45 6 3 1 6
21 6 2 3 1 46 2 3 2 1
22 6 1 3 5 47 3 1 3 2
23 3 3 2 2 48 4 1 3 2
24 3 1 1 3 49 3 1 1 4
25 4 2 1 3 50 6 2 2 6
Raw data: Saltiness Intensity
Sample 312 Not salty enough JAR Too salty
Frequency 22 13 15
Percentage 44% 26% 30%
X saltiness liking score 3.82 5.15 2.87
Mean drop of "Not enough salty" = X JAR - Xnon-JAR =5.15 - 3.82 = 1.34
Penalty value of "Not enough salty" = 0.44 x 1.34 = 0.59
Mean drop of "Too salty" = X JAR - Xnon-JAR =5.15 - 2.87 = 2.29
Penalty value of "Too salty" = 0.30 x 2.29 = 0.69
Bitterness Intensity
Sample 312 Not Bitter (JAR) Slightly bitter Moderately bitter Strongly bitter
Frequency 0 13 14 23
Percentage 0% 26% 28% 46%
X overall liking score NA 4.15 3.38 2.38
No ideal, we can use JAR of sample 151 (7.81) to calculate Penalty value.
Mean drop of "Slightly bitter" = X JAR - Xnon-JAR = 7.81 - 4.15 = 3.66
Penalty value of "Slightly bitter" = 0.26 x 3.66 = 0.95
Mean drop of "Moderately bitter" = X JAR - Xnon-JAR = 7.81 - 3.38 = 4.43
Penalty value of "Moderatey bitter" = 0.28 x 4.43 = 1.24
Mean drop of "Strongly bitter" = X JAR - Xnon-JAR = 7.81 - 2.38 = 5.43
Penalty value of "Strongly bitter" = 0.46 x 5.43 = 2.50

22. 2020
SAMPLE Non-JAR N % Vote Mean JAR Mean Drop Penalty Value
CONTROL Not salty enough 9 18% 6.11 7.18 1.07 0.19
1% NaCl Too salty 7 14% 4.86 7.18 2.32 0.33
Not bitter 37 74% 7.14 7.14 0.00 NA
Slightly bitter 10 20% 6.10 7.14 1.04 0.21
Moderately bitter 3 6% 5.33 7.14 1.81 0.11
Strongly bitter 0 0%
312 Not salty enough 22 44% 3.82 5.15 1.34 0.59
KCl/AMP Too salty 15 30% 2.87 5.15 2.29 0.69
15:1 Not bitter 0 0%
Slightly bitter 13 26% 4.15 7.14 2.99 0.78
Moderately bitter 14 28% 3.38 7.14 3.76 1.05
Strongly bitter 23 46% 2.38 7.14 4.77 2.19
430 Not salty enough 11 22% 4.45 6.14 1.69 0.37
KCl/L-Arg/AMP Too salty 18 36% 3.78 6.14 2.37 0.85
15:2:1 Not bitter 0 0%
Slightly bitter 22 44% 4.85 7.14 2.29 1.01
Moderately bitter 19 38% 5.14 7.14 2.00 0.76
Strongly bitter 3 6% 4.04 7.14 3.10 0.19
Sensory Role
1. Just-About-Right / Penalty Analysis
Benefit of Use:
• Provide PD with diagnostic information.
• Should ask the JAR of attribute that could be adjusted of ingredients in formulation
Mean Drop Value Meaning
0.0 to -0.99 Very slightly concerning
-1.0 to -1.49 Slightly concerning
-1.5 to -1.99 Concerning
-2.0 to greater Very concerning
Total Penalty Value Meaning
>l0.5l High Impact
>l0.25l Noteworthy
Meaning of Total Penalty Value:
Meaning of “Mean Drop” on 9-point
hedonic scale (ASTM 2009)
Guideline: If % non-JAR > 20%, the penalty analysis will be considered.
Penalty value = mean drop x % non-JAR
response

23. 2121
Saltiness JAR
44%
26%
30%
22%
42%
36%
18%
68%
14%
0%
20%
40%
60%
80%
100%
Distribution
312 KCl/AMP (15:1) 44% 26% 30%
430 KCl/L-Arg/AMP
(15:2:1)
22% 42% 36%
CONTROL 18% 68% 14%
Not salty enough JAR - Saltiness Too salty
Bitterness JAR
0%
26% 28%
46%
0%
44%
38%
74%
6%
20%
6%
0%
0%
20%
40%
60%
80%
100%
Distribution
312 KCl/AMP (15:1) 0% 26% 28% 46%
430 KCl/L-Arg/AMP
(15:2:1)
0% 44% 38% 6%
CONTROL 74% 20% 6% 0%
Not bitter Slightly bitter
Moderately
bitter
Strongly
bitter
Sensory Role
#430 gained higher % Saltiness JAR than #312 #430 gained less % Strongly bitter than #312
Interpretation:
• The KCl: L-Arg: AMP ratio (15:2:1) was worked to mask bitterness, as shown the less % of Strongly bitter
than using KCl+AMP (15:1) from 46% to 6%.
• The sample 430 gained the increasing JAR of saltiness as compared to sample 312 (42% vs 26%).
Discussion:
• How much salty and bitter would be needed to reduce in sample 430?
?
?

24. 2222
Sensory Role
1. Just-About-Right / Penalty Analysis
Limitations:
• Penalty analysis does not indicate the magnitude that needs to be changed.
• Changing the level of some attributes may affect the other attributes because of interaction among
ingredients.
• Adjustment on attribute level could change %JAR. We may do the “Opportunity Analysis” (ASTM
2009)
• The attribute that is asked for JAR should be critical enough to acceptance. If it is not, respondent
may be rated “JAR”.
• Make sure if consumer understands the meaning of the attributes in the questionnaire, e.g. milkiness
JAR and creaminess JAR. Should clarify the meaning attribute as per consumer’s perception.
• Consumers tend to rate the familiar product as just right and other products as either too weak or too
strong (Halo/ Horn effect). Expectation error might be happened. We may add “Ideal Scaling” to
observe the correlation of perceived and ideal intensity (ASTM 2009).

25. 2323ASTM (2009)
i. The saltiness level of this sample is,
1 2 3 4 5 6 7 8 9
very less salty very too salty
ii. The saltiness of the “Ideal” chicken broth.
1 2 3 4 5 6 7 8 9
very less salty very too salty
Sensory Role

26. 2424
Sensory Role
2. Similarity Test: Productivity and Cost Saving
Situation:
A manufacturer may replace a chemical with another substance hoping that the finished product
will maintain the same perceived intensity of certain sensory characteristics.
Objective:
Researcher wants to determine if two samples are sufficiently similar to be used
interchangeably.
Concern:
• Why do they need a greater (appropriate) number of panelists?
Because it gives more statistic power to detect a significant difference
• What is the proper Pd, β and α for the sensitive and applicable way?

27. 2525
2. Similarity Test: Type II Error = the risk that we accept the false hypothesis
An ice cream manufacturer wants to substitute the expensive vanilla flavor used
in its premium vanilla ice cream with a cheaper vanilla flavor. However, the
manufacturer does not want the consumer to perceive a difference in the
product.
H0: A = B
HA: A ≠ B
• The data indicate that the null hypothesis should not be rejected.
• For this case Type II Error should be minimized (power of test should be
maximized, power = 1 - β), so that sensory scientist can state with some
confidence that the samples are not perceptibly different.
• The proper test sensitivity parameters: β = 0.001, 0.01 Pd = 30% andα=
0.1, 0.2 depends on the reasonable limit of panelist. Table 17.8 (Meilgaard, 2007)

28. 2626
Calculation of Pmax and Pmin for Similarity Test
Pc (proportion of correct) = c/n
Pd (proportion distinguishers) = 1.5 Pc – 0.5
Sd (standard deviation of Pd) = 1.5 √ Pc (1-Pc)/n
Pmax, One-sided upper confidence limit = Pd + Zβ Sd
Pmin, One-sided lower confidence limit = Pd - Zα Sd
Zα and Zβ are critical values of the standard normal distribution .
Commonly used values of z for one-tailed confidence limits included:
Confidence level z
75% 0.674
80% 0.842
85% 1.036
90% 1.282
95% 1.645
99% 2.326
(Meilgaard, 2007)
Pmax = the possible maximum proportion of population that can distinguish the samples.
Pmin = the possible minimum proportion of population that can distinguish the samples.

29. 2727
Example: Similarity test of milk powder
Triangle Test ( Control vs Test)
Results: Correct Number out of 86 Thai Mums for each control vs test
Remark: The critical value of N = 86 is 37 at α = 0.2
Pair #
Test Lot
no.
no. of
correct
answer Conclusion Pc Pd Std error
Pmax, Upper
confidence limit
(β = 0.01)
Pmin, Lower
confidence limit
(α = 0.2)
C - T1 FU10 35 NS 0.41 0.11 0.079 30% 4%
C - T2 HU18 42 SIG 0.49 0.23 0.081 42% 16%
C - T3 ET27 34 NS 0.40 0.09 0.079 28% 3%
C - T4 FU02 37 SIG 0.43 0.15 0.080 33% 8%
C - T5 GU03 34 NS 0.40 0.09 0.079 28% 3%
C - T6 BT23 32 NS 0.37 0.06 0.078 24% -1%

30. 2828
Conclusion:
1. The HU18 and FU02 lot cannot be replaced the control SMP (DT24)
because the correct numbers were more than 37.
2. Thai mums could not discriminate among test batches of FU10, ET27,
GU03, and BT23. According to Pmax, the 99% (because β = 0.01) sure that
the true proportion of the population that can distinguish the accepted
samples is no greater than:-
3.1 30% for FU10
3.2 28% for ET27
3.3 28% for GU03
3.4 24% for BT23

31. 2929
• ASTM. 2009. Just-About-Right (JAR) Scales: Design, Usage,
Benefits and Risks. Lori Rothman and Merry Jo Parker (Editors).
ASTM international manual series; MNL 63.
• Hasted, A., 2006. Advanced Statistical Procedures and Designs
for Consumer and Sensory Data Analysis and Interpretation. Qi
Statistics Ltd., Reading UK. 340 p.
• Lawless, H.T. and Heymann, H., 1998. Sensory evaluation of
food principles and practices. Chapman & Hall, New York, 827 p.
• Meilgaard, M, Civille, GV, Carr, BT. 2007. Sensory evaluation
techniques, 4th ed. CRC, Press LLC N.W., 448 p.

32. 3030
Sensory Role

33. 31

34. 3232
Overall liking (TOP 3 + BOTTOM 4)
0%
82%
46%
58%
2%
14%
0%
20%
40%
60%
80%
100%
Control Sample 312 Sample 430
TOP 3 TOP 3
TOP 3
Mean score: 6.82 + 0.98 a 3.10 + 1.47 c 4.48 + 1.74 b
(1-9)
a
c
c
b
a
b
Saltiness liking (TOP 3 + BOTTOM 4)
6%
70%
58%
56%
2%
10%
0%
20%
40%
60%
80%
100%
Control Sample 312 Sample 430
TOP 3
TOP 3
TOP 3
Mean score: 4.31 + 1.41 a 2.87 + 1.61 c 3.53 + 1.35 b
(1-9)
c
b
c
b
a
a
Acceptability results of chicken broth:
a, b, c mean the results show significant differences at 95% confidence interval (C.I.)
Analysis:
• Liking score (1-9): ANOVA at 95% C.I.
• TOP 3/ BOTTOM 4: McNemar test (2x2 table) at 95% C.I.

35. 3333
This picture shows the possible
risks and opportunities
associated with changing a
product attribute level based on
the penalty analysis.
Opportunity Analysis: ASTM (2009)
Opportunity Analysis - Orange Juice
74%, 92%
84%, 81%
65%, 85%
94%, 69%
50%
75%
100%
50% 75% 100%
sweetness
orange flavor
sourness
smoothness
Risk, %
Opportunity, %
High risk, low
opportunity
Low risk, high
opportunity

36. 3434
Product
likers
Product
dislikers
Attributes likers B C
Attribute dislikers A D
Total B + A C + D
Product Likers
A –
Accept product,
don’t like attribute
Attribute likers
C –
Reject product,
like attribute
B –
Accept pdt,
like attribute
D – Reject product, don’t like
attribute
Risk = B / (A + B) x 100%
Opportunity = D/ (C+D) x 100%
ASTM (2009)

37. 3535
Sensory Role
Z score = the distance from the cutoff to the mean of each distribution, which relates to the
proportion of the distance in standard deviation units.
(Lawless, 1998)

38. 3636
If we point any value in the normal curve, we can calculate the probability of interest by using the equation.
z = (x - µ)………………………..(1) When z is the critical value of a standard normal variable,
б as x = any value, µ = mean, б = std. deviation
Calculate z, and looking at Table 17.2 (Meilgaard, 2007), the figure in table is the proportion of interest.
• z score will indicate if the sample data is
within 95% C.I. or not.
• There is a significant difference at 95%
C.I. if the z score of sample data is larger
than 1.96

39. 3737
Diagrams below will illustrate how effect size, alpha, and beta interact.
αβ
(Lawless, 1998)

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