Recent Advances in Sensory Science

Chris Findlay & John CasturaFebruary 23, 2016CIFST

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• Increased discrimination test power

• Temporal Check-All-That-Apply (TCATA)

• Individual Differences

Overview

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Increased discrimination test power

Neither the Triangle and Tetrad test methods required the nature of the difference to be stated, but the Tetrad test requires dramatically fewer assessors to achieve the same statistical power.

3

Sensory Discrimination Testing

vs.

triangle

tetrad

Triangle test method

Select the odd sample.

Select the cookie that is different.

N total responses

x correct responses

p0 chance probability (1/3)

A

A Bδ

383 719 540

Select the cookie that is different.

Select the cookie that is different.

702 619 451

Tetrad test method

Group samples into 2 groups of 2

by similarity

Tetrad test method

Claim: provides better operational power than the triangle test.

N total responses

x correct responses

p0 chance probability (1/3)

383 719 540 266

Group samples into 2 groups of 2

by similarity.

Sample sizes (α=0.05; power=0.8)

Delta Tetrad Triangle

1.00 65 220

1.25 34 102

1.50 20 57

Ennis, J.M. & Jesionka, V. (2011). The Power of Sensory Discrimination Methods Revisited. Journal of Sensory Studies, 26, 371-382.

Temporal Check-All-That-Apply (TCATA)

TCATA summary

• Extends CATA to continuously track sensory properties.

• Builds on earlier non-intensity methods

(Flavor Profile, Time-Quality Tracking, Temporal

Dominance of Sensations, Temporal Order of

Sensations, …)

• Used with trained panelists or consumers

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:20

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:00

Timer starts when Startbutton is clicked

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:01

Check/uncheck attributes to describe the sample

…immediately

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:15

Check/uncheck attributes to describe the sample…

or after a waiting period (e.g. after swallowing)

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:01

Instructions related to evaluation protocol could be provided

Swallow the sample now…

TCATA question

Check and uncheck words to track changes in the orange juice. At each moment, the words that are checkedshould describe the orange juice (check all that apply, in that moment).

Astringent

Sourness

Sweetness

Off Flavor

Bitterness

Orange Flavor

0:45

Evaluation ends at a set time (determined by study objectives)

TCATA raw data

Eggy

Sweetness

Milk flavor

Hard

Icy

Soft

Time (seconds)

Natural Vanilla

Smooth texture

Artificial vanilla

Buttery

TCATA reference lines

Product trajectories

Refereed Publications

Ares, G., Jaeger, S. R., Antúnez, L., Vidal, L, Giménez, A., Coste, B., Picallo, A., & Castura, J. C. (2015). Comparison of TCATA and TDS for dynamic sensory characterization of food products, Food Research International, 78, 148-158. http://dx.doi.org/10.1016/j.foodres.2015.10.023

Boinbaser, L., Parente, M. E., Castura, J. C., & Ares, G. (2015). Dynamic sensory characterization of cosmetic creams during application using Temporal Check-All-That-Apply (TCATA) questions. Food Quality and Preference, http://dx.doi.org/10.1016/j.foodqual.2015.05.003

Castura, J. C., Antúnez, L., Giménez, A., & Ares, G. (2016). Temporal Check-all-that-apply (TCATA): A novel dynamic method for characterizing products. Food Quality and Preference, http://dx.doi.org/10.1016/j.foodqual.2015.06.017

Oliveira, D., Antúnez, L., Giménez, A., Castura, J. C., Deliza, R., & Ares, G. (2015). Sugar reduction in probiotic chocolate-flavored milk: Impact on dynamic sensory profile and liking. Food Research International, http://dx.doi.org/10.1016/j.foodres.2015.05.050

Selected Conference Presentations

Castura, J. C., Baker, A. K., & Ross, C. F. (2015). Characterizing wine finish using TCATA product contrails. In 1st Afrosense Conference. 23-26 November. Stellenbosch, South Africa. (Submitted abstract).

Castura, J. C., King, S. C., Li, Q., & Serrano, D. (2015). Using Temporal Check-All-That-Apply (TCATA) to understand the relationship among hedonic, emotion, and sensory attributes. In 11th Pangborn Sensory Science Symposium. 23-27 August. Gothenburg, Sweden. Scientific Poster Presentation. (Forthcoming).

Open access

Individual Differences

Is it possible to create products for individuals?

New Basic Tastes

Oleogustus and Kokumi

Understanding Genetic variation

Specific Anosmia

Super Tasters and Bitter Blindness

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What do we know ?

• There is no product that is “universally” liked, even water.

• Optimization of products is essential to achieve efficiency and market success

• To optimize, you must have a clear target.

• Unless you segment your consumers based upon their sensory preference you will not have a clear target.

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3.00 4.00 5.00 6.00 7.00 8.00 9.00

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

Cabernet Sauvignon Mean Liking

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3.00 4.00 5.00 6.00 7.00 8.00 9.00

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

3.00 4.00 5.00 6.00 7.00 8.00 9.00

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

3.00 4.00 5.00 6.00 7.00 8.00 9.00

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

3.00 4.00 5.00 6.00 7.00 8.00 9.00

W1

W2

W3

W4

W5

W6

W7

W8

W9

W10

W11

W12

Cluster 1 – 28% Cluster 2 – 23%

Cluster 3 –32% Cluster 4 – 17%

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GPA of 16 Whole Grain Breads 55 Sensory Attributes

-1.10 1.10

-0.50

0.50

Variance Accounted for:DIM1 – 66%DIM2 – 9%DIM3 – 7%DIM4 – 4%

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3 4 5 6 7 8 9

Overall LikingAll 570 Category Consumers

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7.1

6.8

6.6

6.5

6.4

6.2

5.9

5.9

5.8

5.8

5.7

5.5

5.5

5.2

5.0

7.3

LOVE IT!

HATE IT!

2 3 4 5 6 7 8 9

Cluster 1 Overall Liking 25.8 % 147 Consumers

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7.4

6.7

6.7

6.6

6.5

6.1

5.8

6.0

4.9

5.6

4.9

4.4

4.5

4.4

3.2

7.8

LOVE IT!

HATE IT!

3 4 5 6 7 8 9

Cluster 2 Overall Liking 45.3 % 258 Consumers

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7.9

7.8

7.7

7.5

7.3

7.0

6.6

6.9

6.3

6.5

6.0

5.9

5.9

5.7

5.6

8.0

LOVE IT!

HATE IT!

3 4 5 6 7 8 9

Cluster 3 Overall Liking 28.9 %165 Consumers

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6.9

6.2

6.1

5.9

5.7

5.5

5.3

5.5

4.6

4.9

4.6

4.5

4.5

4.5

3.7

7.6

LOVE IT!

HATE IT!

• Consumer segmentation is essential to understand liking

• This applies to all consumers

• Even little ones

Understanding Consumers is important!

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PC

2

PC 1

Product Liking Clusters

X2

B1

G8

G1

G4X1

G2

Products that are found close together are in the same liking cluster

CLUSTER 1

CLUSTER 2

Strawberry Jam Consumer Liking Clusters

Chunksof

Fruit

Smooth and

Sweet

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Conclusions

• We must be aware of the increase in our understanding of perception and the methods for measuring the eating and drinking experience.

• Statistical tools allow the results we obtain to be interpreted with greater confidence.

• It’s probably impractical to tailor products for individuals

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Thank You