0
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.com
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.comThank you to our sponsor & partners!Gold...
11Dudsadee ArchakraisornSensory Laboratory ManagerM.Sc. (Food Science)Certificate Program of “Applied Sensory and Consumer...
22• How PD attain success in innovation with insightsfrom sensory research.• What are the new trends in sensory research?•...
33
44We test our recipe andWe can make itIdea of “Healthy Pizza”Initial Consumer validationIDEAS We develop the recipeon “HO...
55 Acceptability/Preference Home Use Test Driver of Liking Positioning in market Usage and AttitudeIDEAS Acceptabili...
66Preference Mapping & Clustering
77Preference Mapping and Preference Clustering.• To investigate market segmentation (preference clustering) and sensory dr...
881. First we calculate the average of eachvariable (avg. liking score of 6 pdts for X1,X2,…X100)2. The mean-centering pro...
99Preference Mapping and Preference Clustering: Key Terms of PCAa. Eigenvalue reflects the quality of the projection from ...
1010Preference Mapping and Preference Clustering• Step of conducting preference mapping;a) Prepare commercial products.b) ...
1111How to read the map? (Hasted, 2006) The cosine of the angle betweenthe two arrows joining attributesto the origin is ...
12Variables Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-Gfruity 0.069 0.157 0.177 0.384 -0.899 0.323 -0.212floral 0.072 0.156 ...
1313Normalized Liking Scores for each Cluster Segment-1.00-0.500.000.501.00Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-GGlobal...
1414 Acceptability/Preference Home Use Test Driver of Liking Positioning in market Usage and AttitudeIDEAS Acceptabi...
1515Nutrient profileGO/NO GOPilot TrialPlant TrialQualificationSensoryEvaluationProductFormulationGO/NO GOProduct Develope...
1616Typical Project’s Success CriteriaFor example,• Overall acceptability of new prototype is parity with competitor’sprod...
17171. Just-About-Right/ Penalty Analysis2. Similarity Test
18181. Just-About-Right / Penalty Analysis (ASTM, 2009)• Use to determine the optimum levels of attributes in a product.• ...
1919Sensory RoleSample312Saltiness liking S JAR B JAROveralllikingSample312Saltiness liking S JAR B JAROverallliking1 6 1 ...
2020SAMPLE Non-JAR N % Vote Mean JAR Mean Drop Penalty ValueCONTROL Not salty enough 9 18% 6.11 7.18 1.07 0.191% NaCl Too ...
2121Saltiness JAR44%26%30%22%42%36%18%68%14%0%20%40%60%80%100%Distribution312 KCl/AMP (15:1) 44% 26% 30%430 KCl/L-Arg/AMP(...
2222Sensory Role1. Just-About-Right / Penalty AnalysisLimitations:• Penalty analysis does not indicate the magnitude that ...
2323ASTM (2009)i. The saltiness level of this sample is,1 2 3 4 5 6 7 8 9very less salty very too saltyii. The saltiness o...
2424Sensory Role2. Similarity Test: Productivity and Cost SavingSituation:A manufacturer may replace a chemical with anoth...
25252. Similarity Test: Type II Error = the risk that we accept the false hypothesisAn ice cream manufacturer wants to sub...
2626Calculation of Pmax and Pmin for Similarity TestPc (proportion of correct) = c/nPd (proportion distinguishers) = 1.5 P...
2727Example: Similarity test of milk powderTriangle Test ( Control vs Test)Results: Correct Number out of 86 Thai Mums for...
2828Conclusion:1. The HU18 and FU02 lot cannot be replaced the control SMP (DT24)because the correct numbers were more tha...
2929• ASTM. 2009. Just-About-Right (JAR) Scales: Design, Usage,Benefits and Risks. Lori Rothman and Merry Jo Parker (Edito...
3030Sensory Role
31
3232Overall liking (TOP 3 + BOTTOM 4)0%82%46%58%2%14%0%20%40%60%80%100%Control Sample 312 Sample 430TOP 3 TOP 3TOP 3Mean s...
3333This picture shows the possiblerisks and opportunitiesassociated with changing aproduct attribute level based onthe pe...
3434ProductlikersProductdislikersAttributes likers B CAttribute dislikers A DTotal B + A C + DProduct LikersA –Accept prod...
3535Sensory RoleZ score = the distance from the cutoff to the mean of each distribution, which relates to theproportion of...
3636If we point any value in the normal curve, we can calculate the probability of interest by using the equation.z = (x -...
3737Diagrams below will illustrate how effect size, alpha, and beta interact.αβ(Lawless, 1998)
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.comThank you to our sponsor & partners!Gold...
Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.com
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Insights from Sensory Research - How this Leads to Fresh Ideas and Innovation - MeadJohnson

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

  1. 1. Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.com
  2. 2. Insight Valley Asia 2013 | May 16 & 17 | Bangkok, Thailand | www.insightvalley.comThank you to our sponsor & partners!Gold SponsorSupported byOrganised by
  3. 3. 11Dudsadee ArchakraisornSensory Laboratory ManagerM.Sc. (Food Science)Certificate Program of “Applied Sensory and Consumer Science” from UC Davis Extension (2012).How this Leads to Fresh Ideas and Innovationon 17 May 2013 in Bangkok, Thailand
  4. 4. 22• How PD attain success in innovation with insightsfrom sensory research.• What are the new trends in sensory research?• What sensory research reveals and how this helps inproduct development.
  5. 5. 33
  6. 6. 44We test our recipe andWe can make itIdea of “Healthy Pizza”Initial Consumer validationIDEAS We develop the recipeon “HOW” to make itWe set up financial, andcost/margin target We ask resources tomake itINITIATION DEVELOPMENTLet make many of PizzaLAUNCH and SHIP Launch andFollow up the launchresultPOST LAUNCHEVALUATION1stSHIPIDEAGENERATIONIDEAAPPROVALGATEPROJECTAPPROVALGATELAUNCHAPPROVALGATEPROJECTCLOSEGATEStage 1 Stage 2 Stage 3 Stage 4Gate 1 Gate 2 Gate 3 Gate 4Innovation ProcessSensory RoleModified from: http://www.prod-dev.com/stage-gate.php
  7. 7. 55 Acceptability/Preference Home Use Test Driver of Liking Positioning in market Usage and AttitudeIDEAS Acceptability/Preference Just Right DiscriminationINITIATION DEVELOPMENT DiscriminationLAUNCH and SHIP POST LAUNCHEVALUATION1stSHIPIDEAGENERATIONIDEAAPPROVALGATEPROJECTAPPROVALGATELAUNCHAPPROVALGATEPROJECTCLOSEGATEStage 1 Stage 2 Stage 3 Stage 4What would we know in each step?Sensory RoleModified from: http://www.prod-dev.com/stage-gate.php
  8. 8. 66Preference Mapping & Clustering
  9. 9. 77Preference Mapping and Preference Clustering.• To investigate market segmentation (preference clustering) and sensory drivers ofliking (internal preference mapping).• Using multivariate methods of analysis (PCA – Principal Component Analysis).• To find new directions (dimensions or principal components) in themultidimensional space of observations that display most variation amongobservations.• To find loadings plots that approximate the correlation or covariance matrices inthat space.New TrendsX1 X2 X3 … X100 Global CL1 CL2 CL3 fruity floral sour citric citrus oil cookedProduct A 5 6 4 … 2 5.29 5.53 2.55 5.68 8.91 8.90 1.81 4.02 4.58 2.49Product B 5 5 1 … 6 5.10 5.20 2.64 5.51 9.53 9.48 1.65 4.00 4.57 2.52Observations 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 2.58Product E 2 4 3 … 7 4.71 2.47 3.55 6.07 2.05 2.06 0.90 2.50 3.10 2.62Product F 3 4 2 … 5 5.50 5.33 2.91 6.07 10.71 10.58 1.52 3.64 4.19 2.49Active Variables = Liking score (N = 100) Supplement Variables; sensory dataSupplement; Global Liking, CL1, CL2, CL3
  10. 10. 881. First we calculate the average of eachvariable (avg. liking score of 6 pdts for X1,X2,…X100)2. The mean-centering procedure correspondsto moving the co-ordinate system.4. The direction of PC1 is given by the cosine ofthe angles αx1,αx2, andαx3. These valuesindicate how the variables x1, x2, and x3 “load”into PC1. Hence, they are called loadings.New Trends3.1 The first principal component is the line in X-space that best approximates the data (leastsquares). 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. 11. 99Preference Mapping and Preference Clustering: Key Terms of PCAa. Eigenvalue reflects the quality of the projection from the N-dimensional initial table to a lowernumber 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 F6Eigenvalue 26.702 15.982 15.575 14.818 13.089 10.835Variability (%) 27.528 16.476 16.056 15.276 13.494 11.170Cumulative % 27.528 44.004 60.060 75.336 88.830 100.000• The first two or three eigenvalueswill correspond to a high % of thevariance, ensuring us that the mapsbased on the first two or three factorsare a good quality projection of theinitial multi-dimensional table.New TrendsSquared cosines of the variables:F1 F2 F3 F4 F5 F6Global Liking 0.700 0.159 0.000 0.040 0.022 0.078CL1 = 30 0.903 0.000 0.078 0.000 0.000 0.019CL2 = 11 0.002 0.475 0.384 0.058 0.004 0.077CL3 = 59 0.269 0.087 0.024 0.443 0.172 0.005fruity 0.667 0.000 0.173 0.095 0.038 0.026floral 0.688 0.000 0.165 0.087 0.036 0.024sour 0.980 0.008 0.005 0.006 0.000 0.000citric 0.783 0.000 0.087 0.110 0.002 0.018citrus oil 0.769 0.002 0.096 0.107 0.006 0.021cooked 0.448 0.062 0.049 0.074 0.299 0.068watery 0.084 0.064 0.708 0.000 0.003 0.141syrup 0.238 0.190 0.189 0.101 0.147 0.136astringen 0.039 0.087 0.161 0.573 0.115 0.025rancid 0.146 0.305 0.052 0.105 0.392 0.000sweet 0.207 0.247 0.060 0.083 0.293 0.109smooth 0.011 0.184 0.594 0.000 0.007 0.203bitter 0.928 0.014 0.056 0.000 0.001 0.001artificial 0.169 0.156 0.105 0.123 0.434 0.012Pdt-A 0.083 0.588 0.001 0.058 0.249 0.021Pdt-B 0.004 0.007 0.277 0.178 0.267 0.267Pdt-C 0.019 0.074 0.013 0.092 0.148 0.654Pdt-D 0.066 0.046 0.091 0.601 0.178 0.017Pdt-E 0.928 0.014 0.056 0.000 0.001 0.001Pdt-F 0.001 0.128 0.461 0.185 0.202 0.023Pdt-G 0.065 0.309 0.267 0.052 0.123 0.184
  12. 12. 1010Preference 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 ofproduct 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. 13. 1111How to read the map? (Hasted, 2006) The cosine of the angle betweenthe two arrows joining attributesto the origin is approximating thecorrelation between the attributes. Two arrows are in the samedirection indicate the attributes arepositively correlated. Two arrows in the oppositedirection indicate the attributes arenegatively correlated. The length of the arrow from theorigin to the unit circle indicateshow well the variation in thatattribute is being explained.Interpretation:• Preference is towards those products that have floral, fruity, sour (eg. Pdt C, Pdt G, and Pdt F), andaway from Pdt E, which was found bitter.• Strongly “Positive” driver of liking are fruity, floral, sour.• Strongly “Negative” driver of liking is bitter.New TrendsVariables (axes F1 and F2: 44.00 %)Pdt-GPdt-FPdt-EPdt-DPdt-CPdt-BPdt-Aartificialbittersmoothsweetrancidastringensyrupwaterycookedcitrus oilcitricsourfloral fruityCL3 = 59CL2 = 11CL1 = 30Global Liking-1-0.75-0.5-0.2500.250.50.751-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1F1 (27.53 %)F2(16.48%)Active variables Supplementary variables
  14. 14. 12Variables Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-Gfruity 0.069 0.157 0.177 0.384 -0.899 0.323 -0.212floral 0.072 0.156 0.180 0.376 -0.910 0.315 -0.190sour 0.371 0.143 0.121 0.194 -0.959 -0.053 0.184citric 0.328 0.307 0.311 -0.112 -0.922 0.019 0.069citrus oil 0.335 0.323 0.319 -0.100 -0.915 0.010 0.028cooked -0.296 -0.104 0.204 0.357 0.703 -0.304 -0.560watery 0.158 0.216 0.072 -0.331 0.101 0.581 -0.796syrup 0.398 0.437 0.440 -0.045 -0.537 -0.132 -0.562astringen -0.077 -0.236 -0.236 0.986 -0.073 -0.236 -0.130rancid 0.929 -0.228 -0.228 -0.228 -0.228 -0.228 0.209sweet 0.051 -0.131 -0.161 -0.326 -0.413 0.064 0.917smooth -0.298 0.195 0.446 -0.337 -0.350 0.694 -0.350bitter -0.167 -0.167 -0.167 -0.167 1.000 -0.167 -0.167artificial -0.831 0.248 0.248 0.248 0.248 0.248 -0.410VariablesGlobalLiking CL1 = 30 CL2 = 11 CL3 = 59fruity 0.768 0.917 -0.238 -0.353floral 0.779 0.926 -0.223 -0.362sour 0.776 0.958 -0.051 -0.604citric 0.704 0.939 -0.034 -0.740citrus oil 0.685 0.936 -0.059 -0.755cooked -0.616 -0.665 -0.020 0.281watery -0.216 0.006 -0.612 0.006syrup 0.223 0.625 -0.346 -0.794astringen 0.098 0.054 -0.151 0.225rancid 0.120 0.283 -0.191 -0.283sweet 0.506 0.326 0.456 -0.041smooth 0.379 0.385 -0.042 -0.066bitter -0.864 -0.988 0.017 0.502artificial -0.180 -0.274 0.044 0.24812Correlation matrix(Pearsons (n)):1) Liking vs Sensory data2) Liking vs Product3) Product vs Sensory dataVariables Global Liking CL1 = 30 CL2 = 11 CL3 = 59Pdt-A 0.002 0.267 -0.430 -0.314Pdt-B -0.282 0.131 -0.389 -0.669Pdt-C 0.331 0.212 0.587 -0.279Pdt-D 0.181 0.144 -0.145 0.182Pdt-E -0.864 -0.988 0.017 0.502Pdt-F 0.316 0.185 -0.267 0.502Pdt-G 0.316 0.049 0.627 0.076• Select Pdt C for productlaunch.• Use the data in 2) todraw the normalized likingscore of each cluster (seethe next slide).• Gap of improvement for Pdt C would be increasing fruity, floral, sour, and reducing bitter.New Trends
  15. 15. 1313Normalized Liking Scores for each Cluster Segment-1.00-0.500.000.501.00Pdt-A Pdt-B Pdt-C Pdt-D Pdt-E Pdt-F Pdt-GGlobal LikingCL1 = 30CL2 = 11CL3 = 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 = 59Positive: fruity, floral, sour fruity, floral, sour, citric,citrus oilNA NANegative: bitter bitter NA citrus oil, syrupNew Trends
  16. 16. 1414 Acceptability/Preference Home Use Test Driver of Liking Positioning in market Usage and AttitudeIDEAS Acceptability/Preference Just Right DiscriminationINITIATION DEVELOPMENT DiscriminationLAUNCH and SHIP POST LAUNCHEVALUATION1stSHIPIDEAGENERATIONIDEAAPPROVALGATEPROJECTAPPROVALGATELAUNCHAPPROVALGATEPROJECTCLOSEGATEStage 1 Stage 2 Stage 3 Stage 4What would we know in each step?Sensory RoleModified from: http://www.prod-dev.com/stage-gate.php
  17. 17. 1515Nutrient profileGO/NO GOPilot TrialPlant TrialQualificationSensoryEvaluationProductFormulationGO/NO GOProduct DeveloperPilot TeamsSensory ScientistInnovation TeamsProcess EngineerAnalytical LabScientistLAUNCHNoNoYesYesSuccessful Innovation
  18. 18. 1616Typical Project’s Success CriteriaFor example,• Overall acceptability of new prototype is parity with competitor’sproduct.• Product’s shelf-life as targeted.• Taste improvement; with significant less BOTTOM 4.Successful Innovation
  19. 19. 17171. Just-About-Right/ Penalty Analysis2. Similarity Test
  20. 20. 18181. 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 directionalinformation 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’-adenosinemonophosphate) have a synergic effect notonly in inhibit undesirable bitterness taste, butalso enhancing saltiness taste in KCl solutions.
  21. 21. 1919Sensory RoleSample312Saltiness liking S JAR B JAROveralllikingSample312Saltiness liking S JAR B JAROverallliking1 6 1 3 3 26 6 2 3 22 7 2 1 7 27 2 1 3 23 4 3 1 3 28 6 2 3 24 6 2 2 3 29 2 1 3 25 4 3 3 3 30 3 1 2 26 5 1 2 5 31 4 2 1 47 2 1 3 2 32 2 3 2 28 2 3 3 2 33 6 1 1 59 4 3 3 3 34 2 1 3 210 6 2 1 6 35 4 2 2 311 3 3 3 3 36 6 1 1 512 4 1 2 3 37 6 1 2 313 3 3 3 3 38 2 3 2 314 4 1 3 3 39 4 1 1 115 4 1 2 3 40 3 1 3 416 1 3 3 1 41 3 3 1 317 6 2 2 6 42 2 3 3 118 4 2 3 2 43 2 1 1 419 2 2 3 2 44 4 1 3 420 2 3 2 3 45 6 3 1 621 6 2 3 1 46 2 3 2 122 6 1 3 5 47 3 1 3 223 3 3 2 2 48 4 1 3 224 3 1 1 3 49 3 1 1 425 4 2 1 3 50 6 2 2 6Raw data: Saltiness IntensitySample 312 Not salty enough JAR Too saltyFrequency 22 13 15Percentage 44% 26% 30%X saltiness liking score 3.82 5.15 2.87Mean drop of "Not enough salty" = X JAR - Xnon-JAR =5.15 - 3.82 = 1.34Penalty value of "Not enough salty" = 0.44 x 1.34 = 0.59Mean drop of "Too salty" = X JAR - Xnon-JAR =5.15 - 2.87 = 2.29Penalty value of "Too salty" = 0.30 x 2.29 = 0.69Bitterness IntensitySample 312 Not Bitter (JAR) Slightly bitter Moderately bitter Strongly bitterFrequency 0 13 14 23Percentage 0% 26% 28% 46%X overall liking score NA 4.15 3.38 2.38No 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 = 3.66Penalty value of "Slightly bitter" = 0.26 x 3.66 = 0.95Mean drop of "Moderately bitter" = X JAR - Xnon-JAR = 7.81 - 3.38 = 4.43Penalty value of "Moderatey bitter" = 0.28 x 4.43 = 1.24Mean drop of "Strongly bitter" = X JAR - Xnon-JAR = 7.81 - 2.38 = 5.43Penalty value of "Strongly bitter" = 0.46 x 5.43 = 2.50
  22. 22. 2020SAMPLE Non-JAR N % Vote Mean JAR Mean Drop Penalty ValueCONTROL Not salty enough 9 18% 6.11 7.18 1.07 0.191% NaCl Too salty 7 14% 4.86 7.18 2.32 0.33Not bitter 37 74% 7.14 7.14 0.00 NASlightly bitter 10 20% 6.10 7.14 1.04 0.21Moderately bitter 3 6% 5.33 7.14 1.81 0.11Strongly bitter 0 0%312 Not salty enough 22 44% 3.82 5.15 1.34 0.59KCl/AMP Too salty 15 30% 2.87 5.15 2.29 0.6915:1 Not bitter 0 0%Slightly bitter 13 26% 4.15 7.14 2.99 0.78Moderately bitter 14 28% 3.38 7.14 3.76 1.05Strongly bitter 23 46% 2.38 7.14 4.77 2.19430 Not salty enough 11 22% 4.45 6.14 1.69 0.37KCl/L-Arg/AMP Too salty 18 36% 3.78 6.14 2.37 0.8515:2:1 Not bitter 0 0%Slightly bitter 22 44% 4.85 7.14 2.29 1.01Moderately bitter 19 38% 5.14 7.14 2.00 0.76Strongly bitter 3 6% 4.04 7.14 3.10 0.19Sensory Role1. Just-About-Right / Penalty AnalysisBenefit of Use:• Provide PD with diagnostic information.• Should ask the JAR of attribute that could be adjusted of ingredients in formulationMean Drop Value Meaning0.0 to -0.99 Very slightly concerning-1.0 to -1.49 Slightly concerning-1.5 to -1.99 Concerning-2.0 to greater Very concerningTotal Penalty Value Meaning>l0.5l High Impact>l0.25l NoteworthyMeaning of Total Penalty Value:Meaning of “Mean Drop” on 9-pointhedonic scale (ASTM 2009)Guideline: If % non-JAR > 20%, the penalty analysis will be considered.Penalty value = mean drop x % non-JARresponse
  23. 23. 2121Saltiness JAR44%26%30%22%42%36%18%68%14%0%20%40%60%80%100%Distribution312 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 saltyBitterness JAR0%26% 28%46%0%44%38%74%6%20%6%0%0%20%40%60%80%100%Distribution312 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 bitterModeratelybitterStronglybitterSensory Role#430 gained higher % Saltiness JAR than #312 #430 gained less % Strongly bitter than #312Interpretation:• The KCl: L-Arg: AMP ratio (15:2:1) was worked to mask bitterness, as shown the less % of Strongly bitterthan 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. 24. 2222Sensory Role1. Just-About-Right / Penalty AnalysisLimitations:• 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 amongingredients.• Adjustment on attribute level could change %JAR. We may do the “Opportunity Analysis” (ASTM2009)• The attribute that is asked for JAR should be critical enough to acceptance. If it is not, respondentmay be rated “JAR”.• Make sure if consumer understands the meaning of the attributes in the questionnaire, e.g. milkinessJAR 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 toostrong (Halo/ Horn effect). Expectation error might be happened. We may add “Ideal Scaling” toobserve the correlation of perceived and ideal intensity (ASTM 2009).
  25. 25. 2323ASTM (2009)i. The saltiness level of this sample is,1 2 3 4 5 6 7 8 9very less salty very too saltyii. The saltiness of the “Ideal” chicken broth.1 2 3 4 5 6 7 8 9very less salty very too saltySensory Role
  26. 26. 2424Sensory Role2. Similarity Test: Productivity and Cost SavingSituation:A manufacturer may replace a chemical with another substance hoping that the finished productwill maintain the same perceived intensity of certain sensory characteristics.Objective:Researcher wants to determine if two samples are sufficiently similar to be usedinterchangeably.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. 27. 25252. Similarity Test: Type II Error = the risk that we accept the false hypothesisAn ice cream manufacturer wants to substitute the expensive vanilla flavor usedin its premium vanilla ice cream with a cheaper vanilla flavor. However, themanufacturer does not want the consumer to perceive a difference in theproduct.H0: A = BHA: 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 bemaximized, power = 1 - β), so that sensory scientist can state with someconfidence 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. 28. 2626Calculation of Pmax and Pmin for Similarity TestPc (proportion of correct) = c/nPd (proportion distinguishers) = 1.5 Pc – 0.5Sd (standard deviation of Pd) = 1.5 √ Pc (1-Pc)/nPmax, One-sided upper confidence limit = Pd + Zβ SdPmin, One-sided lower confidence limit = Pd - Zα SdZα and Zβ are critical values of the standard normal distribution .Commonly used values of z for one-tailed confidence limits included:Confidence level z75% 0.67480% 0.84285% 1.03690% 1.28295% 1.64599% 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. 29. 2727Example: Similarity test of milk powderTriangle Test ( Control vs Test)Results: Correct Number out of 86 Thai Mums for each control vs testRemark: The critical value of N = 86 is 37 at α = 0.2Pair #Test Lotno.no. ofcorrectanswer Conclusion Pc Pd Std errorPmax, Upperconfidence limit(β = 0.01)Pmin, Lowerconfidence 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. 30. 2828Conclusion: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 thatthe true proportion of the population that can distinguish the acceptedsamples is no greater than:-3.1 30% for FU103.2 28% for ET273.3 28% for GU033.4 24% for BT23
  31. 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 Designsfor Consumer and Sensory Data Analysis and Interpretation. QiStatistics Ltd., Reading UK. 340 p.• Lawless, H.T. and Heymann, H., 1998. Sensory evaluation offood principles and practices. Chapman & Hall, New York, 827 p.• Meilgaard, M, Civille, GV, Carr, BT. 2007. Sensory evaluationtechniques, 4th ed. CRC, Press LLC N.W., 448 p.
  32. 32. 3030Sensory Role
  33. 33. 31
  34. 34. 3232Overall liking (TOP 3 + BOTTOM 4)0%82%46%58%2%14%0%20%40%60%80%100%Control Sample 312 Sample 430TOP 3 TOP 3TOP 3Mean score: 6.82 + 0.98 a 3.10 + 1.47 c 4.48 + 1.74 b(1-9)accbabSaltiness liking (TOP 3 + BOTTOM 4)6%70%58%56%2%10%0%20%40%60%80%100%Control Sample 312 Sample 430TOP 3TOP 3TOP 3Mean score: 4.31 + 1.41 a 2.87 + 1.61 c 3.53 + 1.35 b(1-9)cbcbaaAcceptability 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. 35. 3333This picture shows the possiblerisks and opportunitiesassociated with changing aproduct attribute level based onthe penalty analysis.Opportunity Analysis: ASTM (2009)Opportunity Analysis - Orange Juice74%, 92%84%, 81%65%, 85%94%, 69%50%75%100%50% 75% 100%sweetnessorange flavorsournesssmoothnessRisk, %Opportunity, %High risk, lowopportunityLow risk, highopportunity
  36. 36. 3434ProductlikersProductdislikersAttributes likers B CAttribute dislikers A DTotal B + A C + DProduct LikersA –Accept product,don’t like attributeAttribute likersC –Reject product,like attributeB –Accept pdt,like attributeD – Reject product, don’t likeattributeRisk = B / (A + B) x 100%Opportunity = D/ (C+D) x 100%ASTM (2009)
  37. 37. 3535Sensory RoleZ score = the distance from the cutoff to the mean of each distribution, which relates to theproportion of the distance in standard deviation units.(Lawless, 1998)
  38. 38. 3636If 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. deviationCalculate 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 iswithin 95% C.I. or not.• There is a significant difference at 95%C.I. if the z score of sample data is largerthan 1.96
  39. 39. 3737Diagrams below will illustrate how effect size, alpha, and beta interact.αβ(Lawless, 1998)
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