Insights from Sensory Research - How this Leads to Fresh Ideas and Innovation - MeadJohnson

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


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Organised by

1 1

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

2 2

• 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.

4 4

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

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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

http://www.prod-dev.com/stage-gate.php





5 5

Acceptability/Preference

Home Use Test

Driver of Liking

Positioning in market Usage and Attitude

IDEAS


Just Right

Discrimination


Discrimination

LAUNCH and SHIP


1st

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IDEA

APPROVAL GATE

PROJECT

APPROVAL GATE

LAUNCH

APPROVAL GATE

PROJECT

CLOSE GATE


What would we know in each step?

Sensory Role







6 6

Preference Mapping & Clustering

7 7

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

8 8

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)

9 9

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

10 10

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

11 11

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

smoothsweet

rancid

astringen

syrup

waterycooked

citrus oilcitric

sourfloral 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 (

16.4

8 %

)

Active variables Supplementary variables

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 data 2) 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

13 13

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

14 14


Home Use Test

Driver of Liking

Positioning in market Usage and Attitude

IDEAS


Just Right

Discrimination


Discrimination

LAUNCH and SHIP


1st

S

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A G

EN

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IDEA

APPROVAL GATE

PROJECT

APPROVAL GATE

LAUNCH

APPROVAL GATE

PROJECT

CLOSE GATE


What would we know in each step?

Sensory Role







15 15

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

16 16

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

17 17

1. Just-About-Right/ Penalty Analysis

2. Similarity Test

18 18

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.

19 19 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

20 20

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


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


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

21 21

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 bitterModerately

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?

?

?

22 22 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).

23 23 ASTM (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

24 24 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?

25 25

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)

26 26

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.

27 27

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%

28 28

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

29 29

• 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.

30 30 Sensory Role

32 32

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

ba

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

cb

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.

33 33

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 Analys is - 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

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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)

35 35 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)

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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

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Diagrams below will illustrate how effect size, alpha, and beta interact.

α β

(Lawless, 1998)


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