using range voting analysis techniques for odor profiling · pdf fileusing range voting...

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Using Range Voting Analysis Techniques for Odor Profiling Data

Kimberly-Clark Corp. Sensometrics 2008

G Keep, W Raynor

Sensometrics Conference

Odor Profiling

Challenges

• Odor complexities

• Unlimited odor “attributes”

• Panel training demands

● practice

● calibration

• Researcher time demands

One Possible Approach

• Olfaction theory

• Hybrid testing method

● odor identification

● similarity ratings

• Unique analysis

● Range Voting approach

● Compositional data

• Utility

Kimberly-Clark Corp.July 21, 2008

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Sensometrics ConferenceKimberly-Clark Corp.July 21, 2008

Mnemonic Theory of Odor Perception

Semantic-EpisodicKnowledge

PatternMatcherEncoder

Engram Store

ControlledAssociator

SensoryIntegrator

OtherSensory

Input

OlfactoryInput

Adapted from

Stevenson & Boakes (2003)


Corresponding Sensory Methodology

Neural odor representations matched to memory encodings.

Better matches leads to more memory activation.

Pattern of activations represent the perception of the odor.

Odors easily confused if memory activations are slight.

Mnemonic Theory Odor Profiling Method

Rate

Identified Odors

Identify

“Matching” Odors

Standard

Odor Referent Set

0 2 4 6 8 10

Similarity Scale

Odor“Notes”

3


Key Method Factors

• Panelists select referent odor notes independently.

• Rate “similarity” to test odor.

• Adjunct elements:

• Odor Groupings / Families

• Overall Intensity Ratings

0 1 2 3 4 5 6 7 8 9 10

Low Mid High

Traditional Intensity Rating (combined odor)


Range Voting with Extras

• Winner-take-all elections

• Assign points to candidates

● Bounded 0-100 scale; can assign 0

● Add up – highest score wins

George Box

Copernicus

Albert Einstein

RA Fisher

Galileo Galilei

Jerzey Neyman

0 100

0 100

0 100

Most Famous ScientistRange Voting

Compositional Data

• Proportional parts of a whole

• Sum constrained to a constant

• Relative information

• Non-linear

e.g. 100%0.40

0.25

0.16

0.10

0.09

4


The Analysis Plan

“Range VotingAnalysis”

Statistical ProductComparisons

Odor Character

Weights

Pre-set

Odor Groupings

Overall Intensity

Ratings

“Families”

Similarity

Ratings

Odor CharacterRaw Data

Raw Data


“Range Voting” Analysis Step 1


• Work on individual similarity ratings

• Can use cumulative density function (CDF)

• PROC RANK (Blomnormalization)

• e Blom Score

Logit Transformation

compositions have distributions that are logistic-normal

within panelists

partially remove rater-to-rater variation

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“Range Voting” Analysis Step 2


Scale the exponents

• Scale e Blom Score

• Odors sum to 100%

• within product x panelist

Can combine over panelists to obtain average odor referent notes


Calculate Proportions (Weights)


0.40

0.25

0.16

0.10

0.09 Fruit

Floral

Citrus

Wood

Other• Base on Scale values

• Pool across odor families

• by sample

• Individual notes

• detailed list

Sample A

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Hypothesis Testing


• Use total odor intensities

• Shift distribution

• Base on proportion

• Per sample x odor family

• Analyze with General Linear Models (ANOVA)

Sample A

-2 -1 0 1 2 3 4 5 6 7 8 9 10

FruitTotal Odor

Intensity

40%

Wood

10%

Intensity


A Useful Method?

• Discriminates Between Products

• Measures Consistently

• Provides Meaningful Info

7


Discriminating

• Historical data

• Discrim. Index

● omega squared

● 0-100

• n = 204

● families x samples

● over 50 studies0

5

10

15

20

25

30

0 10 20 30 40 50 60 70 80 90 100

Omega Squared

Freq

uen

cy


Consistency

• Test-retest reliability

● for good discrim:

● r > 0.90

• No test x sample interactions

• Consistent proportions

● RMS < 6%

Sample 1

Sample 3 Sample 4

18

4

32

6

6

21

12

18

42

7

18

15

34

29

2

8

6

6

1425

2818

6

4

19

7

48

16

29

9

44

8

10

26

4

Sample 2

18% 21%

6%

7% 6%

42%

32%

18%

14%

6%4%

29%

Inner = Test 1

Outer = Test 2

3 9 %

2 3 %

1 1 %

8 %

8 %

6 %5 %0 %

Floral

Wood

Fruit

Ozone

Sweet

TropFruit

Citrus

Green

\

44%48%

6%

6%

18%

4%

8%

8%

34%

12%

25% 7%

9%

18%

18%

16%10%

29%26%

15%

28%

40

23

11

8

8

65

44

23

10

6

6

6

6

6%

8%

8%

6%

6%

6%

10%11%

23%

23%

40% 44%

6%

5%

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Meaningful Information

• What to compare to?

• Aging stability

• Fragrance target confirmation

• Matching fragrances

• Select fragrance submissions

Generalizability

Utility


Summary

• Odor profiling methodology

● less labor intensive

• “Range Voting” analysis

● compositional data

• Demonstrated utility

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Key References

Stevenson RJ., Boakes RA. 2003. A mnemonic theory of odor perception. Psychological Review 110:340–364.

Brams SJ, Fishburn PC. 2007. 2nd ed. Approval voting. Springer. 198p.

Aitchison J. 1986. The statistical analysis of compositional data. Springer. 416p.



Acknowledgements

K-C people involved with the method:

• Rhonda Engle

• Barb Bombinski

• Tami Gaubatz

• Rick Zepp

• Patti Braun

• Mary Stiltjes

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Range Voting Analysis for Odor Profiling Data

ENGRAM STORE

OLFACTORY PROCESSING MODULE

3. CONTROLLED ASSOCIATOR

2a. PATTERN MATCHER

2b. AUTOMATIC

COMPARATORAND ENCODER

5. SEMANTIC AND

EPISODIC KNOWLEDGE4. SENSORY INTEGRATOR

OTHER

SENSORYINPUT

1. OLFACTORY INPUT

Mnemonic Theory of Odor Processing

Stevenson & Boakes (2003)

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