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Bayesian Networks
        A complete framework to
understand consumer perceptions
 and their links with external data




                  Fabien CRAIGNOU, Repères
                    Carole JEGOU, Kraft R&D
Context & Objectives                                                                     2



                PRODUCT TESTING in France.
                    20 market products tested in the confectionary sector
                    Sequential monadic procedure
                    N= 200 respondents
                    Overall liking, JAR questions

                SENSORY PANEL
                    40 significant sensory attributes
                    Covering aroma, texture, flavour, aftertaste and after sensation.



                ANALYTICAL MEASURES
                    20 key variables




      GAIN UNDERSTANDING OF CONSUMER PERCEPTIONS AND DRIVERS OF LIKING,
       AND PROVIDE THE R&D WITH GUIDELINES FOR PRODUCT DEVELOPMENT.




                         Sensometrics meeting – Rotterdam – July 2010
Data processing workflow: 3-step analysis                                                            3




   STEP 1 : UNDERSTANDING                                                   STEP 2 : SIMPLIFYING
   CONSUMER PERCEPTIONS                                                    SENSORY & TECHNICAL
                                                                               INFORMATION
       « internal » model
      consumer attributes                                                   Identification of main
          (JAR scales)                                                           dimensions



                          STEP 3 : INFLUENCE OF
                        TECHNICAL DIMENSIONS ON
                         CONSUMER PERCEPTIONS

                                   « external » model
                                   sensory attributes,
                                     analytical data
                            Sensometrics meeting – Rotterdam – July 2010
Data processing workflow: 3-step analysis                                  4




   STEP 1 : UNDERSTANDING
   CONSUMER PERCEPTIONS

       « internal » model
      consumer attributes
          (JAR scales)




                            Sensometrics meeting – Rotterdam – July 2010
Understanding consumer perceptions                                                                    STEP 1     5


    AUTOMATIC LEARNING - discovering STRUCTURE and PARAMETERS

                                  Colour JAR

                                            41%                                                  Mutual information

       Taste of X JAR       36%            OVERALL
                                                                             Texture 1 JAR
                                            LIKING

                                                        46%            27%              25%

             42%            49%
                                            54%
                                                        Texture 2 JAR            32%   Consistency JAR

                         14%       Sweetness JAR
      Aftertaste JAR



       HEURISTIC SEARCH ALGORITHM TO TEST DIFFERENT STRUCTURES

       QUALITY OF THE POSSIBLE NETWORKS IS ASSESSED BY A SCORE TAKING INTO ACCOUNT

            The fit of the model to the data

            The complexity of the structure

       CROSS VALIDATION IS USED TO ENSURE ROBUSTNESS


                                  Sensometrics meeting – Rotterdam – July 2010
Understanding consumer perceptions                                                                         STEP 1   6


    OUTPUT – relative weights in overall liking


    Sweetness                                                      24%
                                                                                 Relative Weights in
                                                                                   overall Liking
                                                                                    (Mutual information
     Aftertaste                                               22%                normalized to sum 100%)



       Texture                                            20%


       Colour                                        18%


    Taste of X                                   16%


           ALL DRIVERS ARE VERY CLOSE IN TERMS OF IMPACT ON TASTE LIKING
           (contrary to other markets where 1 or 2 drivers prevail).

           OVERALL LIKING requires good performances on…

                   … TASTE dimensions (aftertaste, sweetness, taste of…)

                   … TEXTURE perception

                   …COLOUR perception
                                  Sensometrics meeting – Rotterdam – July 2010
Understanding consumer perceptions                                                                                                    STEP 1   7


    OUTPUT – detailed impact of intensity balance
   Impact of sweetness balance                                             Impact texture balance


         Probability that Liking >= 8                                              Probability that liking >= 8

        80%                                                                       80%

        60%        57%                                                            60%
                                                                                             40%
        40%                                                                       40%
                                too light                                                                 too light
        20%                                  sweetness                            20%                                     meltiness
                     11%        JAR                                                               5%      JAR
               5%                            Intensity                                      2%                            Intensity
        0%                      too strong                                         0%                     too strong




   Impact of Aftertaste balance                                              Impact of colour intensity balance


         Probability that Liking >= 8                                                Probability that Liking >= 8

        80%                                                                         80%

        60%                                                                         60%
                   43%                                                                           43%
        40%                                                                         40%
                               too light                                                                      too light
        20%                                  aftertaste                             20%            13%                      colour
                     4%        JAR                                                                            JAR
              1%                             Intensity                                       3%                             balance
        0%                     too strong                                            0%                       too dark




                                             Sensometrics meeting – Rotterdam – July 2010
Data processing workflow: 3-step analysis                                                            8




   STEP 1 : UNDERSTANDING                                                   STEP 2 : SIMPLIFYING
   CONSUMER PERCEPTIONS                                                    SENSORY & TECHNICAL
                                                                               INFORMATION
       « internal » model
      consumer attributes                                                   Identification of main
          (JAR scales)                                                           dimensions




                            Sensometrics meeting – Rotterdam – July 2010
Simplifying sensory/analytical information                                                       STEP 2      9


     Handling sensory and analytical variables

       Sensory and Analytical variables have been discretized into 3 levels using a K-Means
       procedure, in order to adapt each discretization to the distribution of the variable.



                                                                CHECKING CORRESPONDENCE WITH
                                                             GROUPINGS BASED ON THE SENSORY PANEL
                                                               Falvour Attribute 1
Sensory Score    Flavour Attribute 1
                                                               Product 1             29.55   a
                                                               Product 2             26.75   a
                                                               Product 3             25.94   ab
                                                               Product 4             25.75   ab
                                                               Product 5             22.38     b c
                                                               Product 6             21.98     b c d
                                                               Product 7             21.73     b c d
                                                               Product 8             21.36       c d
                                                               Product 9             21.33       c d
                                                               Product 10            21.11       c d
                                                               Product 11            21.02       c d
                                                               Product 12            20.31       c d
                                                               Product 13            19.91       c d
                                                               Product 14            19.20       c d   e
                                                               Product 15            19.15       c d   e
                                                               Product 16            18.58       c d   e
                                                               Product 17            18.57       c d   e
                                                               Product 18            17.95       c d   e
                                                               Product 19            17.25         d   e
                                                               Product 20            14.94             e f

                                        Number of
                                       observations


                                  Sensometrics meeting – Rotterdam – July 2010
Simplifying sensory/analytical information                                                                                                               STEP 2   10


     Identifying main dimensions                               Unsupervised learning
                                                               Hierarchical Clustering based on KL divergence
                              Aroma 1
                              Aroma 1                          IMPORTANCE OF CROSS-VALIDATION

                                                                                                                          Mouthfeel 3
                                                                                                                          Mouthfeel 3
                                                           Descriptor 8
                                                            Descriptor 8                    Descriptor 11
                                                                                             Descriptor 11

                                                            Descriptor 9                 Descriptor 12
                                                             Descriptor 9                 Descriptor 12
                                                                                                           Descriptor 13
                                                                                                            Descriptor 13
         Mouthfeel 1
         Mouthfeel 1                                         Descriptor 10
                                                                                      Descriptor 15
                                                                                       Descriptor 15
                                                              Descriptor 10                        Descriptor 14
                                                                                                    Descriptor 14


                       Descriptor 6            Descriptor 7
                        Descriptor 6            Descriptor 7            Flavour 4
                                                                        Flavour 4                            Descriptor 16
                                                                                                              Descriptor 16
                                      Descriptor 5                                                                                             Flavour 3
                                                                                                                                               Flavour 3
                                       Descriptor 5                                           Descriptor 18
                                                                                               Descriptor 18
                                                       Descriptor 33       Descriptor 34
                                                        Descriptor 33       Descriptor 34
                                                                                                                   Descriptor 17
                                                                                            Descriptor 19           Descriptor 17
                                                                                             Descriptor 19
                                                   Descriptor 37        Descriptor 35
                         Descriptor 4               Descriptor 37        Descriptor 35
                          Descriptor 4
                                                               Descriptor 36
                                                                Descriptor 36
                   Descriptor 3
                    Descriptor 3

                                   Descriptor 2                                   Descriptor 29
              Descriptor 1          Descriptor 2                                   Descriptor 29                                               Descriptor 36
               Descriptor 1                                                                                      Descriptor 20                  Descriptor 36
                                                                                   Descriptor 30                  Descriptor 20
                                                                                    Descriptor 30
Mouthfeel 2
Mouthfeel 2                                                                   Descriptor 31                      Descriptor 21
                                              Descriptor 26                    Descriptor 31                      Descriptor 21
                                               Descriptor 26
                                                                            Descriptor 32          Descriptor 23              Descriptor 22
                                               Descriptor 27                 Descriptor 32          Descriptor 23              Descriptor 22
                                                Descriptor 27
                                           Descriptor 28                                                                                              Flavour 2
                                                                                                                                                      Flavour 2
                                            Descriptor 28   After sensation 1 Descriptor 24
                                                            After sensation 1 Descriptor 24
                    Aroma 2
                    Aroma 2                                                                     Descriptor 25
                                                                                                 Descriptor 25         Flavour 1
                                                                                                                       Flavour 1
                                               Sensometrics meeting – Rotterdam – July 2010
Data processing workflow: 3-step analysis                                                            11




   STEP 1 : UNDERSTANDING                                                   STEP 2 : SIMPLIFYING
   CONSUMER PERCEPTIONS                                                    SENSORY & TECHNICAL
                                                                               INFORMATION
       « internal » model
      consumer attributes                                                   Identification of main
          (JAR scales)                                                           dimensions



                          STEP 3 : INFLUENCE OF
                        TECHNICAL DIMENSIONS ON
                         CONSUMER PERCEPTIONS

                                   « external » model
                                   sensory attributes,
                                     analytical data
                            Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                                                                     STEP 3        12


              Key issues of the modeling workflow



                            Consumer attributes                        Sensory
                                                                       Dimensions
                                                                                           In order to let the search algorithm
                             c1                          s1
             Consumer 1
                                             cn                             sk             focus on the links between Consumer
                                                                                           Data and Sensory Data…
Product 1




                                                        Constant for 1 product


             Consumer 200
                                                                                           FIXING the arcs between consumer
                                                                                           dimensions (already discovered)


                                                                                           FORBID the arcs between sensory
                                                                                           dimensions (links are too obvious: for
                                                                                           each product => 200 times the same
                                                                                           sensory variables)
             Consumer 1
Product 20




             Consumer 200




                                            Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                                                        STEP 3   13


    Structural model
                                       Colour JAR



                 Taste of X JAR           OVERALL LIKING
                                                                                 Texture 1 JAR


 Flavour 4
 Flavour 4

                                                             Texture 2 JAR                 Consistency JAR

              Aftertaste JAR           Sweetness JAR


                           Flavour 2                              Flavour 3
                                                                  Flavour 3
                           Flavour 2                                                 Mouthfeel 3
                                                                                     Mouthfeel 3          Mouthfeel 1
                                                                                                          Mouthfeel 1
                                                   After
                                                    After
                                                sensation 1
                                                 sensation 1
               Aroma 2                                                                     Mouthfeel 2
                                                                                           Mouthfeel 2
               Aroma 2
                                  Flavour 1
                                  Flavour 1


             Aroma 1
             Aroma 1



                                  Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                                                                                   STEP 3   14


    OUTPUT: importance of sensory dimensions on SWEETNESS perception



                       * Flavour 3
                             Bitterness
                                                                                                              Mutual Information :
                                                                                                         non-linear measure of impact.

           * After sensation 1
                 Astringent aftersensation                                                                           Total sample


                                                                                                         Cluster 1   Cluster 2

                       * Flavour 2
                             Sourness




                           Aroma 2
                            Cocao aroma




                          Flavour 1
                                Fruity




                          Flavour 4
                          Chocolate flavour




                           Aroma 1
                            Sweet aroma


                                                                                                 % Mutual
                                              0.0     1.0   2.0   3.0   4.0   5.0   6.0   7.0
                                                                                                information

                                                    Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                                                                                   STEP 3   15


    OUTPUT: expected sensory levels (top-6 descriptors)

              Probability that sweetness is perceived as JAR by consumers
              FLAVOUR 3 - descriptor 1                         FLAVOUR 3 - descriptor 5         FLAVOUR 3 - descriptor 7

        70%                                              70%                                    70%

        60%                                              60%                                    60%
                              62%                                 58%      62%                                     60%
                 56%
                                                                                                                              56%
        50%                                48%
                                                         50%                              48%
                                                                                                50%
                                                                                                            48%

        40%                                              40%                                    40%

        30%                                              30%                                    30%

        20%                                              20%                                    20%
              11 - 18        18 - 23   23 - 28                  12 - 19   19 - 22   22 - 29              8 - 10   10 - 14    > 20

              AFTERSENSATION 1                                 FLAVOUR 2 -descriptor 2          AFTERSENSATION 1
              descriptor 8                                                                      descriptor 12
        70%                                              70%                                    70%

        60%                   63%
                                                         60%                                    60%
                59%                                               60%                                      59%
                                                                           60%                                     59%
        50%                                              50%                                    50%
                                           47%                                            46%                                   47%

        40%                                              40%                                    40%

        30%                                              30%                                    30%

        20%                                              20%                                    20%
              17 - 22        22 - 24   24 - 26                  6 - 10    10 - 12   12 - 18              7 - 13   13 - 17   17 - 23




                  Significant positive impact
                  Significant negative impact
                                                 Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                                                                                      STEP 3    16
                                                                                                                         Preference Clusters
    OUTPUT: expected sensory levels (top-6 descriptors)                                                                     Cluster 1    Cluster 2
    Following 2 of the preference segments identified by KRAFT

              FLAVOUR 3 - descriptor 1                         FLAVOUR 3 - descriptor 5         FLAVOUR 3 - descriptor 7

        70%                                              70%                                    70%
                                63%                                          63%                                    63%
              65%                                                64%                                                             65%
        60%                                              60%                                    60%
                                           58%                                            58%
                              61%                                          61%                              58%
                                                                   53%                                             57%
        50%                                              50%                                    50%
                 48%
                                                                                                                                 48%
        40%                                              40%                                    40%
                                         38%                                          38%                 38%
        30%                                              30%                                    30%

        20%                                              20%                                    20%
              11 - 18        18 - 23   23 - 28                  12 - 19   19 - 22   22 - 29              8 - 10   10 - 14       > 20

              AFTERSENSATION 1                                 FLAVOUR 2 -descriptor 2          AFTERSENSATION 1
              descriptor 8                                                                      descriptor 12
        70%                                              70%                                    70%
                                64%                             63%          63%
              65%                                                                                         65%        60%
        60%                                              60%                                    60%
                              62%
                                                                  58%                                              59%
                                           53%                             58%                                                     53%
        50%     54%                                      50%                              51%   50%        54%


        40%                                              40%                                    40%
                                         41%                                          41%                                        41%

        30%                                              30%                                    30%

        20%                                              20%                                    20%
              17 - 22        22 - 24   24 - 26                   6 - 10   10 - 12   12 - 18              7 - 13   13 - 17      17 - 23

                                                                                                                   Significant positive impact
                                                                                                                   Significant negative impact


                                                 Sensometrics meeting – Rotterdam – July 2010
Sensory expectations of consumers                                            STEP 3       17


    OUTPUT: Summary of expected sensory levels                              Main sample
                                                                            Cluster 1
    KEY DESCRIPTORS                                                         Cluster 2



                  Descriptor 1
                  Descriptor 1

                  Descriptor 2
                  Descriptor 2
  Monitoring…
                    Descriptor 3
                    Descriptor 3
  SWEETNESS
  AFTERTASTE
                    Descriptor 4
                    Descriptor 4
   TASTE OF X

                    Descriptor 5
                    Descriptor 5


                    Descriptor 6
                    Descriptor 6


                    Descriptor 7
                    Descriptor 7

  MOUTHFEEL
                    Descriptor 8
                    Descriptor 8




                             Sensometrics meeting – Rotterdam – July 2010
SUMMARY – ADDED VALUE / LIMITS on the methodology                                 18



    STRUCURAL LEARNING
     possible to discover the links between variables
     enables to have a good overview of all consumer perceptions simultaneously

    NON-LINEAR RELATIONS
     very important when dealing with links between consumer & sensory

    USE OF CROSS-VALIDATION
     to enhance confidence into the models



             Discretizing sensory/analytical variables: tough (not natural?) job,
              need to check correspondence with sensory panel significant
              differences.

             Non-linear relations: exercise caution, as sometimes weird relations
              can be discovered (U-shape like relation for example) => needs to be
              cleaned.

             Causality ?


                            Sensometrics meeting – Rotterdam – July 2010
PLS PATH MODELLING – BAYESIAN NETWORKS                                      19



SOME IMPORTANT DIFFERENCES TO REMEMBER

              BAYESIAN                               PLS PATH MODELLING



        Discover the structure                       Impose the structure


       Latent variables: entirely                     Latent variables
          dependent on the                         constructed to explain
        explanatory variables                            TARGET


          Many observations                             Few observations



                     Sensometrics meeting – Rotterdam – July 2010

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Sensometrics 2010 Repères

  • 1. Bayesian Networks A complete framework to understand consumer perceptions and their links with external data Fabien CRAIGNOU, Repères Carole JEGOU, Kraft R&D
  • 2. Context & Objectives 2  PRODUCT TESTING in France.  20 market products tested in the confectionary sector  Sequential monadic procedure  N= 200 respondents  Overall liking, JAR questions  SENSORY PANEL  40 significant sensory attributes  Covering aroma, texture, flavour, aftertaste and after sensation.  ANALYTICAL MEASURES  20 key variables  GAIN UNDERSTANDING OF CONSUMER PERCEPTIONS AND DRIVERS OF LIKING, AND PROVIDE THE R&D WITH GUIDELINES FOR PRODUCT DEVELOPMENT. Sensometrics meeting – Rotterdam – July 2010
  • 3. Data processing workflow: 3-step analysis 3 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions STEP 3 : INFLUENCE OF TECHNICAL DIMENSIONS ON CONSUMER PERCEPTIONS « external » model sensory attributes, analytical data Sensometrics meeting – Rotterdam – July 2010
  • 4. Data processing workflow: 3-step analysis 4 STEP 1 : UNDERSTANDING CONSUMER PERCEPTIONS « internal » model consumer attributes (JAR scales) Sensometrics meeting – Rotterdam – July 2010
  • 5. Understanding consumer perceptions STEP 1 5 AUTOMATIC LEARNING - discovering STRUCTURE and PARAMETERS Colour JAR 41% Mutual information Taste of X JAR 36% OVERALL Texture 1 JAR LIKING 46% 27% 25% 42% 49% 54% Texture 2 JAR 32% Consistency JAR 14% Sweetness JAR Aftertaste JAR HEURISTIC SEARCH ALGORITHM TO TEST DIFFERENT STRUCTURES QUALITY OF THE POSSIBLE NETWORKS IS ASSESSED BY A SCORE TAKING INTO ACCOUNT The fit of the model to the data The complexity of the structure CROSS VALIDATION IS USED TO ENSURE ROBUSTNESS Sensometrics meeting – Rotterdam – July 2010
  • 6. Understanding consumer perceptions STEP 1 6 OUTPUT – relative weights in overall liking Sweetness 24% Relative Weights in overall Liking (Mutual information Aftertaste 22% normalized to sum 100%) Texture 20% Colour 18% Taste of X 16% ALL DRIVERS ARE VERY CLOSE IN TERMS OF IMPACT ON TASTE LIKING (contrary to other markets where 1 or 2 drivers prevail). OVERALL LIKING requires good performances on…  … TASTE dimensions (aftertaste, sweetness, taste of…)  … TEXTURE perception  …COLOUR perception Sensometrics meeting – Rotterdam – July 2010
  • 7. Understanding consumer perceptions STEP 1 7 OUTPUT – detailed impact of intensity balance Impact of sweetness balance Impact texture balance Probability that Liking >= 8 Probability that liking >= 8 80% 80% 60% 57% 60% 40% 40% 40% too light too light 20% sweetness 20% meltiness 11% JAR 5% JAR 5% Intensity 2% Intensity 0% too strong 0% too strong Impact of Aftertaste balance Impact of colour intensity balance Probability that Liking >= 8 Probability that Liking >= 8 80% 80% 60% 60% 43% 43% 40% 40% too light too light 20% aftertaste 20% 13% colour 4% JAR JAR 1% Intensity 3% balance 0% too strong 0% too dark Sensometrics meeting – Rotterdam – July 2010
  • 8. Data processing workflow: 3-step analysis 8 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions Sensometrics meeting – Rotterdam – July 2010
  • 9. Simplifying sensory/analytical information STEP 2 9 Handling sensory and analytical variables Sensory and Analytical variables have been discretized into 3 levels using a K-Means procedure, in order to adapt each discretization to the distribution of the variable. CHECKING CORRESPONDENCE WITH GROUPINGS BASED ON THE SENSORY PANEL Falvour Attribute 1 Sensory Score Flavour Attribute 1 Product 1 29.55 a Product 2 26.75 a Product 3 25.94 ab Product 4 25.75 ab Product 5 22.38 b c Product 6 21.98 b c d Product 7 21.73 b c d Product 8 21.36 c d Product 9 21.33 c d Product 10 21.11 c d Product 11 21.02 c d Product 12 20.31 c d Product 13 19.91 c d Product 14 19.20 c d e Product 15 19.15 c d e Product 16 18.58 c d e Product 17 18.57 c d e Product 18 17.95 c d e Product 19 17.25 d e Product 20 14.94 e f Number of observations Sensometrics meeting – Rotterdam – July 2010
  • 10. Simplifying sensory/analytical information STEP 2 10 Identifying main dimensions Unsupervised learning Hierarchical Clustering based on KL divergence Aroma 1 Aroma 1 IMPORTANCE OF CROSS-VALIDATION Mouthfeel 3 Mouthfeel 3 Descriptor 8 Descriptor 8 Descriptor 11 Descriptor 11 Descriptor 9 Descriptor 12 Descriptor 9 Descriptor 12 Descriptor 13 Descriptor 13 Mouthfeel 1 Mouthfeel 1 Descriptor 10 Descriptor 15 Descriptor 15 Descriptor 10 Descriptor 14 Descriptor 14 Descriptor 6 Descriptor 7 Descriptor 6 Descriptor 7 Flavour 4 Flavour 4 Descriptor 16 Descriptor 16 Descriptor 5 Flavour 3 Flavour 3 Descriptor 5 Descriptor 18 Descriptor 18 Descriptor 33 Descriptor 34 Descriptor 33 Descriptor 34 Descriptor 17 Descriptor 19 Descriptor 17 Descriptor 19 Descriptor 37 Descriptor 35 Descriptor 4 Descriptor 37 Descriptor 35 Descriptor 4 Descriptor 36 Descriptor 36 Descriptor 3 Descriptor 3 Descriptor 2 Descriptor 29 Descriptor 1 Descriptor 2 Descriptor 29 Descriptor 36 Descriptor 1 Descriptor 20 Descriptor 36 Descriptor 30 Descriptor 20 Descriptor 30 Mouthfeel 2 Mouthfeel 2 Descriptor 31 Descriptor 21 Descriptor 26 Descriptor 31 Descriptor 21 Descriptor 26 Descriptor 32 Descriptor 23 Descriptor 22 Descriptor 27 Descriptor 32 Descriptor 23 Descriptor 22 Descriptor 27 Descriptor 28 Flavour 2 Flavour 2 Descriptor 28 After sensation 1 Descriptor 24 After sensation 1 Descriptor 24 Aroma 2 Aroma 2 Descriptor 25 Descriptor 25 Flavour 1 Flavour 1 Sensometrics meeting – Rotterdam – July 2010
  • 11. Data processing workflow: 3-step analysis 11 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions STEP 3 : INFLUENCE OF TECHNICAL DIMENSIONS ON CONSUMER PERCEPTIONS « external » model sensory attributes, analytical data Sensometrics meeting – Rotterdam – July 2010
  • 12. Sensory expectations of consumers STEP 3 12 Key issues of the modeling workflow Consumer attributes Sensory Dimensions In order to let the search algorithm c1 s1 Consumer 1 cn sk focus on the links between Consumer Data and Sensory Data… Product 1 Constant for 1 product Consumer 200 FIXING the arcs between consumer dimensions (already discovered) FORBID the arcs between sensory dimensions (links are too obvious: for each product => 200 times the same sensory variables) Consumer 1 Product 20 Consumer 200 Sensometrics meeting – Rotterdam – July 2010
  • 13. Sensory expectations of consumers STEP 3 13 Structural model Colour JAR Taste of X JAR OVERALL LIKING Texture 1 JAR Flavour 4 Flavour 4 Texture 2 JAR Consistency JAR Aftertaste JAR Sweetness JAR Flavour 2 Flavour 3 Flavour 3 Flavour 2 Mouthfeel 3 Mouthfeel 3 Mouthfeel 1 Mouthfeel 1 After After sensation 1 sensation 1 Aroma 2 Mouthfeel 2 Mouthfeel 2 Aroma 2 Flavour 1 Flavour 1 Aroma 1 Aroma 1 Sensometrics meeting – Rotterdam – July 2010
  • 14. Sensory expectations of consumers STEP 3 14 OUTPUT: importance of sensory dimensions on SWEETNESS perception * Flavour 3 Bitterness Mutual Information : non-linear measure of impact. * After sensation 1 Astringent aftersensation Total sample Cluster 1 Cluster 2 * Flavour 2 Sourness Aroma 2 Cocao aroma Flavour 1 Fruity Flavour 4 Chocolate flavour Aroma 1 Sweet aroma % Mutual 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 information Sensometrics meeting – Rotterdam – July 2010
  • 15. Sensory expectations of consumers STEP 3 15 OUTPUT: expected sensory levels (top-6 descriptors) Probability that sweetness is perceived as JAR by consumers FLAVOUR 3 - descriptor 1 FLAVOUR 3 - descriptor 5 FLAVOUR 3 - descriptor 7 70% 70% 70% 60% 60% 60% 62% 58% 62% 60% 56% 56% 50% 48% 50% 48% 50% 48% 40% 40% 40% 30% 30% 30% 20% 20% 20% 11 - 18 18 - 23 23 - 28 12 - 19 19 - 22 22 - 29 8 - 10 10 - 14 > 20 AFTERSENSATION 1 FLAVOUR 2 -descriptor 2 AFTERSENSATION 1 descriptor 8 descriptor 12 70% 70% 70% 60% 63% 60% 60% 59% 60% 59% 60% 59% 50% 50% 50% 47% 46% 47% 40% 40% 40% 30% 30% 30% 20% 20% 20% 17 - 22 22 - 24 24 - 26 6 - 10 10 - 12 12 - 18 7 - 13 13 - 17 17 - 23 Significant positive impact Significant negative impact Sensometrics meeting – Rotterdam – July 2010
  • 16. Sensory expectations of consumers STEP 3 16 Preference Clusters OUTPUT: expected sensory levels (top-6 descriptors) Cluster 1 Cluster 2 Following 2 of the preference segments identified by KRAFT FLAVOUR 3 - descriptor 1 FLAVOUR 3 - descriptor 5 FLAVOUR 3 - descriptor 7 70% 70% 70% 63% 63% 63% 65% 64% 65% 60% 60% 60% 58% 58% 61% 61% 58% 53% 57% 50% 50% 50% 48% 48% 40% 40% 40% 38% 38% 38% 30% 30% 30% 20% 20% 20% 11 - 18 18 - 23 23 - 28 12 - 19 19 - 22 22 - 29 8 - 10 10 - 14 > 20 AFTERSENSATION 1 FLAVOUR 2 -descriptor 2 AFTERSENSATION 1 descriptor 8 descriptor 12 70% 70% 70% 64% 63% 63% 65% 65% 60% 60% 60% 60% 62% 58% 59% 53% 58% 53% 50% 54% 50% 51% 50% 54% 40% 40% 40% 41% 41% 41% 30% 30% 30% 20% 20% 20% 17 - 22 22 - 24 24 - 26 6 - 10 10 - 12 12 - 18 7 - 13 13 - 17 17 - 23 Significant positive impact Significant negative impact Sensometrics meeting – Rotterdam – July 2010
  • 17. Sensory expectations of consumers STEP 3 17 OUTPUT: Summary of expected sensory levels Main sample Cluster 1 KEY DESCRIPTORS Cluster 2 Descriptor 1 Descriptor 1 Descriptor 2 Descriptor 2 Monitoring… Descriptor 3 Descriptor 3 SWEETNESS AFTERTASTE Descriptor 4 Descriptor 4 TASTE OF X Descriptor 5 Descriptor 5 Descriptor 6 Descriptor 6 Descriptor 7 Descriptor 7 MOUTHFEEL Descriptor 8 Descriptor 8 Sensometrics meeting – Rotterdam – July 2010
  • 18. SUMMARY – ADDED VALUE / LIMITS on the methodology 18  STRUCURAL LEARNING possible to discover the links between variables enables to have a good overview of all consumer perceptions simultaneously  NON-LINEAR RELATIONS very important when dealing with links between consumer & sensory  USE OF CROSS-VALIDATION to enhance confidence into the models  Discretizing sensory/analytical variables: tough (not natural?) job, need to check correspondence with sensory panel significant differences.  Non-linear relations: exercise caution, as sometimes weird relations can be discovered (U-shape like relation for example) => needs to be cleaned.  Causality ? Sensometrics meeting – Rotterdam – July 2010
  • 19. PLS PATH MODELLING – BAYESIAN NETWORKS 19 SOME IMPORTANT DIFFERENCES TO REMEMBER BAYESIAN PLS PATH MODELLING Discover the structure Impose the structure Latent variables: entirely Latent variables dependent on the constructed to explain explanatory variables TARGET Many observations Few observations Sensometrics meeting – Rotterdam – July 2010