Perceptual Mappingand

Opportunity IdentificationDr. Chris FindlayCompusense Inc.

What are we trying to accomplish?

Outline• Sensory experience of consumers• Descriptive Analysis• What is a Perceptual Map?

– Some examples• Finding holes in the map

– Opportunity identification• A real world example

– Wieners and losers

Sensory Experienceof Consumers

• Consumers judge food on• Appearance• Aroma• Flavor• Texture• After – Experience

• Remember there is no such thing as an “Average Consumer”.

DESCRIPTIVE ANALYSIS

DEFINITION:• any method to describe and quantify the

sensory characteristics of stimuli by a panel of trained assessors.

ASTM Standards 2002, E253 – 02

DESCRIPTIVE ANALYSISPROCEDURE

Identification of the key sensory attributes.

Development of trained panel.

Development of the ballot.

Measurement of the attributes.

Analysis and interpretations of results.

Best practices in Descriptive Analysis

• The Sensory Order– Identify the attribute– Rank its intensity– Scale the intensity

• Feedback Calibration– Assure that results are historically reproducible

A Perceptual Map• Visualization of the

sensory properties of a group of products

• Multidimensional presentation

• Simplification• Many different

methods using factor analysis (PCA, GPA)

CHOC CHIPS

CHOCOLATE FLAVOR

Sweetness

HARDNESS CHEWINESS

1

2

3

4

Overall Goals

• Learning how the products in a class are perceived with respect to strengths weaknesses and similarities

• Learning what potential buyers want• Learning how to produce or modify a

product to optimize its appeal

Lawless & Heymann, 1998

Desired Qualities in Perceptual Mapping

• Goodness of fit (low stress)• Reliability: blind duplicates plot together

– Similar pairs (batches) plot nearby• Dimensionality: model has a few dimensions

– can be plotted• Interpretation: Map should make sense• Validity: map should relate to descriptive attributes

– Should relate to consumer preferences• Payoff: Map should suggest new hypotheses or may

confirm previous hypotheses• Cost efficiency: data collection is rapid and simple

Lawless & Heymann, 1998

A Study of Beef Striploins

• Are there sensory differences ?• By country of origin

– US, Canada, NZ and Australia• By grade and age of animal• 12 products, 12 panelist, 3 replicates• 6 sensory attributes

Multi-Country Study of Beef striploinby animal age and grade

Tender

Juicy

Chewy

Moist

Chew Time

0

2

4

6

8

CND USA <14 >21-28 A SELECTNZ Australia >14-21 >28 AAA CHOICE

Tender

Juicy

Chewy

MoistChew Time

CND

NZ USA

Australia

by Country

Tender

Juicy

ChewyMoist

Chew Time<14

>14-21

>21-28

>28

By Age

Tender

Juicy

ChewyMoist

Chew Time

A

AAASELECT

CHOICE

by Grade

Marinated Rainbow trout

• Improve yield and consumer acceptance

• How much marinade to use?

• Control, Atlantic and Pacific Salmon with 5 prototype products

• 12 panelists, 3 replicates, 6 attributes

GPA of Marinated Trout

Flakiness

Firmness

Fish flavor

SaltJuiciness

Overall flavor

Pacific

Control

Atlantic

15

10

9.57.5

6

Where’s the opportunity?

Category assessment - Ham

• Evaluation of the sensory attributes of a wide range of commercial product.

• 15 products• 10 trained panelists• 3 replicates• 22 attributes (much more complex)

Ham Data

4.694.44.044.64.554.7344.314.534.544.644.235.354.214.54Juiciness

5.274.7865.295.425.085.015.444.673.975.175.354.425.184.57Fiber Awareness

5.195.115.825.444.795.385.065.924.824.785.285.424.885.194.57Chewiness

5.064.524.234.895.244.84.463.694.284.214.474.234.394.735.12Cohesiveness

2.462.242.732.222.342.82.892.882.652.592.732.72.12.722.61Astringency

1.230.440.971.111.061.310.760.860.661.141.061.611.011.040.74Pork Fat

2.281.431.751.551.831.691.381.771.341.651.32.391.511.811.45Pork Flavor

0.580.360.720.550.821.430.450.290.530.290.830.30.570.310.19Phenolic

1.290.472.11.012.131.031.311.823.062.381.550.881.570.920.91Wood Smoke

2.231.342.112.182.362.441.932.11.891.892.412.392.032.231.78Sourness

2.254.562.182.32.392.792.452.282.12.72.382.43.742.452.65Sweetness

5.264.635.435.625.245.595.795.496.124.985.895.795.3265.12Saltiness

0.136.153.220.444.455.441.772.786.833.560.441.876.953.09Firmness

8.477.688.228.327.199.18.199.646.677.859.18.419.17.937.09Surface Shine

1.222.621.881.281.551.792.11.581.511.371.7121.562.512.5Sweet Aromatic

3.451.461.743.181.561.911.510.880.962.061.371.941.681.631.11Sulfur

H15H14H13H12H11H10H9H8H7H6H5H4H3H2H1

HAM - PCA

Sulfur

Sweet Aromatic

Surface Shine

Firmness

Salt

SweetnessSour

Wood Smoke

Phenolic Pork FlavorPork Fat

Astringency

Cohesiveness

Chewiness

Fiber Awareness

Juiciness

H1

H2

H3

H4H5

H6

H7

H8H9

H10H11

H12

H13

H14

H15

Where’s the hole?

A Real-World Case Study

• A category assessment of 14 wieners• Descriptive analysis by 10 panelists• 3 replicates• 23 attributes

• Followed by a consumer study

Wiener Attributes

• Sweet• Poultry flavor• Pork flavor• Beef flavor• Afterburn/Heat• Pepper• Garlic flavor• Green herbs• Smoke flavor• Smoke aroma• Color• Mottled appearance

• Residual oiliness• Juiciness• Particles• Coarseness• Cohesiveness• Firmness• Springiness• Skin chew• Firm skin• Sour• Salt

** Highlighted samples P<0.05

Summary Wiener Data

5.474.614.684.225.264.824.944.355.414.584.994.015.744.03Residual oiliness

6.834.256.654.786.474.86.214.176.384.917.084.057.384.47Juiciness

4.764.985.435.625.145.995.596.145.055.175.296.355.135.84Springiness

5.584.045.853.516.093.125.823.886.143.875.884.415.853.99Skin chew

6.394.86.594.736.324.036.24.246.883.756.54.876.14.47Firm skin

6.785.515.425.335.54.785.775.535.65.535.315.875.255.94Sour

3.353.12.743.83.033.623.444.494.734.794.854.784.444.4Pork flavor

4.983.614.43.734.963.445.813.175.012.484.632.875.163.91After burn/Heat

5.954.595.254.075.594.265.773.816.073.745.74.346.214.49Pepper

5.033.985.644.55.294.054.894.995.624.356.045.165.254.37Garlic flavor

6.455.476.46.166.576.215.46.256.154.836.075.226.355.79Green herbs

4.765.073.184.074.413.374.244.94.394.114.244.335.214.85Smoke flavor

3.133.913.743.874.564.674.315.454.646.243.964.813.233.76Color

P14P13P12P11P10P9P8P7P6P5P4P3P2P1Attribute

Means of Wiener Data

P1

P2

P3

P4

P5

P6

P7

P8

P9

P10

P11

P12

P13

P140

2

4

6

8

Color Green herbs Pepper Pork flavor Firm skin Springiness Residual oilinessSmoke flavor Garlic flavor Heat Sour Skin chew Juiciness

Garlic flavor

4.37

5.25 5.16

6.04

4.35

5.62

4.99 4.89

4.05

5.29

4.50

5.64

3.98

5.03

0

2

4

6

8

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14

Juiciness

4.47

7.38

4.05

7.08

4.91

6.38

4.17

6.21

4.80

6.47

4.78

6.65

4.25

6.83

0

2

4

6

8

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14

Single Attribute Contrasts

Wiener GPA

-0.98 0.98

-0.98

0.98

P14

P13

P12P11

P10

P9

P8

P7

P6

P5P4

P3 P2P1

Color

Smoke

Green herbs

GarlicPepperHeat

Pork

Sour Firm skin

Skin chew

Springiness

Juiciness

Residual oilinessP14P13P12

P11 P10

P9

P8

P7

P6

P5

P4

P3 P2P1

GPA Plot for Wieners

Preference Mappingand

Consumer Target ProductsTom Carr

Carr Consulting