# Descriptive temporal sensory evaluation with consumers: advantages and limitations

Michel Visalli

► To cite this version:

Michel Visalli. Descriptive temporal sensory evaluation with consumers: advantages and limitations. AgroStat 2024, Sep 2024, Bragança, Portugal. hal-04700612

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# Descriptive temporal sensory evaluation with consumers: advantages and limitations

Michel Visalli – michel.visalli@inrae.fr

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Centre des Sciences
du Goût et de
l'Alimentation

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cnrs INRAE

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L'INSTITUT
agro Dijon

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RÉPUBLIQUE
FRANÇAISE

L'agriculture
française
1. Temporal sensory evaluation

2. Literature review on temporal descriptive evaluation methods

3. Focus on temporal evaluation methods used with consumers

4. Introducing retrospective measurement methods to question the grain of temporal data

5. Validity of temporal measurements

6. Precision and resolution of temporal measurements

7. Product discrimination ability of temporal measurements

8. Discussion and recommendations

9. Conclusion

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1

# Temporal sensory evaluation
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<figure><img src="image_10.png" /></figure>

Structure

Composition

Temperature

Portion size

<figure><img src="image_11.png" /></figure>

FOP: destructuring of food in the mouth

Physico-chemical: release of sapid and aromatic compounds

<figure><img src="image_12.png" /></figure>
Controlled parameters

<figure><img src="image_13.png" /></figure>

Information
Presentation
Context

<figure><img src="image_14.png" /></figure>

Structure
Composition
Temperature
Portion size

<figure><img src="image_15.png" /></figure>

FOP: destructuring of food in the mouth
Physico-chemical: release of sapid and aromatic compounds

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>
Controlled parameters

<figure><img src="image_18.png" /></figure>

Information
Presentation
Context

<figure><img src="image_19.png" /></figure>

Structure
Composition
Temperature
Portion size

<figure><img src="image_20.png" /></figure>

Uncontrolled parameters

<figure><img src="image_21.png" /></figure>

Culture
Knowledge
Memory

<figure><img src="image_22.png" /></figure>

Genetics & physiology
Physical state
Mastication behaviour

<figure><img src="image_23.png" /></figure>

FOP: destructuring of food in the mouth
Physico-chemical: release of sapid and aromatic compounds
Brain: Multimodal sensory interactions/cognition

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

Physiological and psychological processes: Stimulation → Identification → Quantification
# Measurements of temporal perception

"Objective" characterization of
food properties by trained panel

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

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## Sensory evaluation methods

Subjective characterization of
food qualities by consumers

<figure><img src="image_29.png" /></figure>

**Analytic methods**
Discriminative tests, quantitative
descriptive analysis

**Affective methods**
Hedonic and preference tests
# Measurements of temporal perception

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

"Objective" characterization of
food properties by trained panel

Subjective characterization of
food qualities by consumers

## Sensory evaluation methods

<figure><img src="image_32.png" /></figure>

**Analytic methods**
Discriminative tests, quantitative
descriptive analysis

<figure><img src="image_33.png" /></figure>

**Rapid methods**
Similarity/comparison tests,
qualitative descriptive analysis

<figure><img src="image_34.png" /></figure>

**Affective methods**
Hedonic and preference tests
# Measurements of temporal perception

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

"Objective" characterization of
food properties by trained panel

Subjective characterization of
food qualities by consumers

## Sensory evaluation methods

<figure><img src="image_37.png" /></figure>

**Analytic methods**
Discriminative tests, quantitative
descriptive analysis

<figure><img src="image_38.png" /></figure>

**Rapid methods**
Similarity/comparison tests,
qualitative descriptive analysis

<figure><img src="image_39.png" /></figure>

**Affective methods**
Hedonic and preference tests

<figure><img src="image_40.png" /></figure>

## Temporal methods
# Literature review on temporal descriptive evaluation methods

## PLOS ONE

OPEN ACCESS

STUDY PROTOCOL

Multi-attribute temporal descriptive methods in sensory analysis applied in food science: Protocol for a scoping review

Michel Visalli 🐦 Mara Virginia Galmarini

Published: July 26, 2022 • https://doi.org/10.1371/journal.pone.0270969

Comprehensive REVIEWS in Food Science and Food Safety

COMPREHENSIVE REVIEW

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Open Access

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Multi-attribute temporal descriptive methods in sensory analysis applied in food science: A systematic scoping review

Michel Visalli 🐦 Mara Virginia Galmarini

First published: 24 January 2024 | https://doi.org/10.1111/1541-4337.13294 | Citations: 1
What are the main methods of temporal sensory evaluation?

<figure><img src="image_46.png" /></figure>

414 articles with TI: first method, still the most used intensity-based method despite the development of multi-attribute variants (in red).
What are the main methods of temporal sensory evaluation?

<figure><img src="image_47.png" /></figure>

414 articles with TI: first method, still the most used intensity-based method despite the development of
multi-attribute variants (in red)

363 articles with multi-attribute temporal methods (27 methods/variants)

Methods most used in research (2022): TDS and TCATA

→ evolution from quantitative to qualitative measurement usable by consumers (no intensity)
How are the temporal methods implemented?

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

What is the underlying motivation? Tendency, cost-efficiency, consumer-research, other?
For what purpose are temporal measurements used in research?

Fundamentals of perception: 5%
For what purpose are temporal measurements used in research?

Fundamentals of perception: 5%

Methodological research: 25%

New method/variant, method comparison,
statistical analyses, etc.

<figure><img src="image_50.png" /></figure>

Some needs still not met?

Publication bias?

Method comparaison often based on qualitative
assessment or restricted to discrimination
For what purpose are temporal measurements used in research?

Fundamentals of perception: 5%

Methodological research: 25%

New method/variant, method comparison,
statistical analyses, etc.

Some needs still not met?

Publication bias?

Method comparison often based on qualitative
assessment or restricted to discrimination

Product characterization: 70%

All product categories

55% combined with other measurements
(descriptive, affective, physico-chemical)

<figure><img src="image_51.png" /></figure>

Choice of the method rarely justified

Real need for temporal sensory evaluation?

Influence of methodologists? (3 authors involved
in 25% of articles)

<figure><img src="image_52.png" /></figure>

Which method for what use?
How are temporal data analyzed?

<table><thead><tr><th>Variable</th><th>By time</th><th>By period</th><th>Global</th></tr></thead><tbody><tr><td colspan="4"><strong>Variable collected or computed at the subject level with intensity-based methods</strong></td></tr><tr><td>Intensity, by attribute*</td><td>I<sub>t</sub></td><td>I<sub>p</sub></td><td>-</td></tr><tr><td>Mean intensity, by attribute</td><td>-</td><td>I<sub>mean</sub></td><td>I<sub>mean</sub></td></tr><tr><td>Max intensity, by attribute</td><td>-</td><td>I<sub>max</sub></td><td>I<sub>max</sub></td></tr><tr><td>TDS score, by attribute (duration std/no std x intensity)*</td><td>-</td><td>D<sub>Ip</sub></td><td>D<sub>IP</sub></td></tr><tr><td>Duration of perception (intensity&gt;0), by attribute*</td><td>-</td><td>D<sub>p</sub></td><td>D<sub>p</sub></td></tr><tr><td colspan="4"><strong>Variable collected or computed at the subject level with qualitative methods</strong></td></tr><tr><td>Citations, by attribute*<br>(dominant/applicable=1, not dominant/not applicable=0)</td><td>C<sub>t</sub></td><td>C<sub>p</sub></td><td>C</td></tr><tr><td>Rank of citation, by attribute*</td><td>RC<sub>t</sub></td><td>-</td><td>RC</td></tr><tr><td>Time to first citation*</td><td>-</td><td>-</td><td>Tfirst</td></tr><tr><td>Time or period to first citation, by attribute*</td><td>-</td><td>-</td><td>T</td></tr><tr><td>Citation, after period or static transformation, by attribute</td><td>-</td><td>C<sub>p</sub></td><td>C</td></tr><tr><td>Number of citations, by attribute</td><td>-</td><td>N<sub>p</sub></td><td>N</td></tr><tr><td>Duration (std/no std) of applicability/dominance, by attribute</td><td>-</td><td>D<sub>p</sub></td><td>D</td></tr><tr><td>Sojourn time (std/no std) of applicability/dominance, by attribute</td><td>-</td><td>S<sub>p</sub></td><td>S</td></tr><tr><td>Number of attributes cited</td><td>-</td><td>Natt<sub>p</sub></td><td>Natt</td></tr><tr><td>Number of citations</td><td>-</td><td>Ncit<sub>p</sub></td><td>Ncit</td></tr><tr><td colspan="4"><strong>Variable collected* or computed at the subject level common to intensity-based and qualitative methods</strong></td></tr><tr><td>Total duration of perception*</td><td>-</td><td>-</td><td>Dtot</td></tr><tr><td>Number of intakes* (if applicable, see Implementation)</td><td>-</td><td>-</td><td>Nint</td></tr><tr><td colspan="4"><strong>Variable computed at the panel level with intensity-based methods</strong></td></tr><tr><td>Mean intensity, by attribute (mean of I<sub>mean</sub>)</td><td>IMEAN<sub>t</sub></td><td>IMEAN<sub>p</sub></td><td>IMEAN</td></tr><tr><td>Max intensity, by attribute (max of IMEAN)</td><td>-</td><td>IMAX<sub>p</sub></td><td>IMAX</td></tr><tr><td colspan="4"><strong>Variable computed at the panel level with qualitative methods</strong></td></tr><tr><td>Citation rate (or frequency), by attribute</td><td>CR<sub>t</sub></td><td>CR<sub>p</sub></td><td>CR</td></tr><tr><td>Mean citation rate, by attribute</td><td>-</td><td>CRMEAN<sub>p</sub></td><td>CRMEAN</td></tr><tr><td>Max citation rate, by attribute</td><td>-</td><td>CRMAX<sub>p</sub></td><td>CRMAX</td></tr><tr><td>Duration above significance (TDS), by attribute</td><td>-</td><td>DSIG<sub>p</sub></td><td>DSIG</td></tr><tr><td colspan="4"><strong>Variable computed at the panel level common to intensity-based and qualitative methods</strong></td></tr><tr><td>Mean duration, by attribute (mean of D)</td><td>-</td><td>DMEAN<sub>p</sub></td><td>DMEAN</td></tr><tr><td>Mean sojourn time, by attribute (mean of S)</td><td>-</td><td>SMEAN<sub>p</sub></td><td>SMEAN</td></tr><tr><td>Number of transitions from one attribute to another, by attribute</td><td>-</td><td>TR<sub>p</sub></td><td>TR</td></tr><tr><td>Area under the curve (CR or IMEAN), by attribute</td><td>-</td><td>AUC<sub>p</sub></td><td>AUC</td></tr><tr><td>Time to CRMAX or IMAX, by attribute</td><td>-</td><td>TMAX<sub>p</sub></td><td>TMAX</td></tr><tr><td>Duration above 90% of CRMAX or IMAX, by attribute</td><td>-</td><td>DMAX<sub>p</sub></td><td>DMAX</td></tr><tr><td>Rate of increase, by attribute</td><td>-</td><td>RINC<sub>p</sub></td><td>RINC</td></tr><tr><td>Rate of decrease, by attribute</td><td>-</td><td>RDEC<sub>p</sub></td><td>RDEC</td></tr></tbody></table>

> 30 variables (21 computed)

> 100 uni/multivariate analyses
## How are temporal data analyzed?

<table><thead><tr><th>Variable</th><th>By time</th><th>By period</th><th>Global</th></tr></thead><tbody><tr><td colspan="4"><strong>Variable collected or computed at the subject level with intensity-based methods</strong></td></tr><tr><td>Intensity, by attribute*</td><td>I<sub>t</sub></td><td>I<sub>p</sub></td><td>-</td></tr><tr><td>Mean intensity, by attribute</td><td>-</td><td>I<sub>mean</sub><sub>p</sub></td><td>I<sub>mean</sub></td></tr><tr><td>Max intensity, by attribute</td><td>-</td><td>I<sub>max</sub><sub>p</sub></td><td>I<sub>max</sub></td></tr><tr><td>TDS score, by attribute (duration std/no std x intensity)*</td><td>-</td><td>D<sub>Ip</sub></td><td>D<sub>I</sub></td></tr><tr><td>Duration of perception (intensity&gt;0), by attribute*</td><td>-</td><td>D<sub>p</sub></td><td>D</td></tr><tr><td colspan="4"><strong>Variable collected or computed at the subject level with qualitative methods</strong></td></tr><tr><td>Citations, by attribute*<br>(dominant/applicable=1, not dominant/not applicable=0)</td><td>C<sub>t</sub></td><td>C<sub>p</sub></td><td>C</td></tr><tr><td>Rank of citation, by attribute*</td><td>RC<sub>t</sub></td><td>-</td><td>RC</td></tr><tr><td>Time to first citation*</td><td>-</td><td>-</td><td>T<sub>first</sub></td></tr><tr><td>Time or period to first citation, by attribute*</td><td>-</td><td>-</td><td>T</td></tr><tr><td>Citation, after period or static transformation, by attribute</td><td>-</td><td>C<sub>p</sub></td><td>C</td></tr><tr><td>Number of citations, by attribute</td><td>-</td><td>N<sub>p</sub></td><td>N</td></tr><tr><td>Duration (std/no std) of applicability/dominance, by attribute</td><td>-</td><td>D<sub>p</sub></td><td>D</td></tr><tr><td>Sojourn time (std/no std) of applicability/dominance, by attribute</td><td>-</td><td>S<sub>p</sub></td><td>S</td></tr><tr><td>Number of attributes cited</td><td>-</td><td>Natt<sub>p</sub></td><td>Natt</td></tr><tr><td>Number of citations</td><td>-</td><td>Ncit<sub>p</sub></td><td>Ncit</td></tr><tr><td colspan="4"><strong>Variable collected* or computed at the subject level common to intensity-based and qualitative methods</strong></td></tr><tr><td>Total duration of perception*</td><td>-</td><td>-</td><td>D<sub>tot</sub></td></tr><tr><td>Number of intakes* (if applicable, see Implementation)</td><td>-</td><td>-</td><td>N<sub>int</sub></td></tr><tr><td colspan="4"><strong>Variable computed at the panel level with intensity-based methods</strong></td></tr><tr><td>Mean intensity, by attribute (mean of I<sub>mean</sub>)</td><td>IMEAN<sub>t</sub></td><td>IMEAN<sub>p</sub></td><td>IMEAN</td></tr><tr><td>Max intensity, by attribute (max of IMEAN)</td><td>-</td><td>IMAX<sub>p</sub></td><td>IMAX</td></tr><tr><td colspan="4"><strong>Variable computed at the panel level with qualitative methods</strong></td></tr><tr><td>Citation rate (or frequency), by attribute</td><td>CR<sub>t</sub></td><td>CR<sub>p</sub></td><td>CR</td></tr><tr><td>Mean citation rate, by attribute</td><td>-</td><td>CRMEAN<sub>p</sub></td><td>CRMEAN</td></tr><tr><td>Max citation rate, by attribute</td><td>-</td><td>CRMAX<sub>p</sub></td><td>CRMAX</td></tr><tr><td>Duration above significance (TDS), by attribute</td><td>-</td><td>DSIG<sub>p</sub></td><td>DSIG</td></tr><tr><td colspan="4"><strong>Variable computed at the panel level common to intensity-based and qualitative methods</strong></td></tr><tr><td>Mean duration, by attribute (mean of D)</td><td>-</td><td>DMEAN<sub>p</sub></td><td>DMEAN</td></tr><tr><td>Mean sojourn time, by attribute (mean of S)</td><td>-</td><td>SMEAN<sub>p</sub></td><td>SMEAN</td></tr><tr><td>Number of transitions from one attribute to another, by attribute</td><td>-</td><td>TR<sub>p</sub></td><td>TR</td></tr><tr><td>Area under the curve (CR or IMEAN), by attribute</td><td>-</td><td>AUC<sub>p</sub></td><td>AUC</td></tr><tr><td>Time to CRMAX or IMAX, by attribute</td><td>-</td><td>TMAX<sub>p</sub></td><td>TMAX</td></tr><tr><td>Duration above 90% of CRMAX or IMAX, by attribute</td><td>-</td><td>DMAX<sub>p</sub></td><td>DMAX</td></tr><tr><td>Rate of increase, by attribute</td><td>-</td><td>RINC<sub>p</sub></td><td>RINC</td></tr><tr><td>Rate of decrease, by attribute</td><td>-</td><td>RDEC<sub>p</sub></td><td>RDEC</td></tr></tbody></table>

Only 20% with consideration for dynamics of perception!

> 30 variables (21 computed)

> 100 uni/multivariate analyses

confirmatory
data analysis

exploratory
data analysis

30% with no consideration for dynamics of perception

<figure><img src="image_53.png" /></figure>

Which analysis for which question?
Focus on temporal evaluation
methods used with consumers
## Pros and cons of temporal methods used with consumers

### Ecological measurements: better explanation of consumers' behaviours?

- Compared to evaluations with trained panels: more representative of consumer perceptions, accounting for individual differences, coupling with affective measurements

- Compared to static retrospective evaluations: measurements closest to the moment of perception, finer grain to study consumers' food quality perception and preferences

### Knowledge built on fragile foundation: too much expectations?

- TDS and TCATA originally developed with trained panels then later transposed to consumer panels, task not straightforward, validity and reliability undocumented: what about data quality?

- Temporal data complex to analyze: subjective interpretation or overinterpretation of the results?
Why is it important to study measurement quality?

**ESN survey**, 57 participants experienced with temporal methods
TDS curves for 3 chocolates: **is A different from B? B from C?** (yes/no/can't say)

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>
Why is it important to study measurement quality?

**ESN survey**, 57 participants experienced with temporal methods
TDS curves for 3 chocolates: **is A different from B? B from C?** (yes/no/can't say)

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>
# Why is it important to study measurement quality?

ESN survey, 57 participants experienced with temporal methods
TDS curves for 3 chocolates: is A different from B? B from C? (yes/no/can't say)

Repeated samples

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

Subjective conclusion based on exploratory data analysis without considering data quality
What are the quality criteria for the temporal measurements?

"Just because one obtains a graphical display or series of tables with associated statistical significance does not mean the results are meaningful and valid."
(Stone et al., 2012)
# What are the quality criteria for the temporal measurements?

"Just because one obtains a graphical display or series of tables with associated statistical significance does not mean the results are meaningful and valid."
(Stone et al., 2012)

Validity
(accuracy)

<figure><img src="image_64.png" /></figure>

How close a given set of observations
are to their true value?

Reliability
(precision)

<figure><img src="image_65.png" /></figure>

<figure><img src="image_66.png" /></figure>

How close the observations are to
each other

<figure><img src="image_67.png" /></figure>
What are the quality criteria for the temporal measurements?

"Just because one obtains a graphical display or series of tables with associated statistical significance does not mean the results are meaningful and valid."
(Stone et al., 2012)

<figure><img src="image_68.png" /></figure>
What are the quality criteria for the temporal measurements?

"Just because one obtains a graphical display or series of tables with associated statistical significance does not mean the results are meaningful and valid."
(Stone et al., 2012)

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

Validity + reliability + resolution required to draw robust conclusions on product differences
<figure><img src="image_71.png" /></figure>
The temporal dimension of data is an important source of variability

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

What is the appropriate grain for examining temporal data?
# Introducing retrospective measurement methods to question the grain of temporal data

<figure><img src="image_74.png" /></figure>

Food Quality and Preference

Volume 85, October 2020, 103956

Concurrent vs. retrospective temporal data collection: Attack-evolution-finish as a simplification of Temporal Dominance of Sensations?

M. Visalli* 🌐, B. Mahieu<sup>b</sup>, A. Thomas<sup>b</sup>, P. Schlich*

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

Food Quality and Preference

Volume 101, October 2022, 104629

Concurrent vs. immediate retrospective temporal sensory data collection: A case study on lemon-flavoured carbonated alcoholic drinks

Michel Visalli*<sup>b</sup>, Takahiro Wakihira<sup>c</sup>, Pascal Schlich<sup>a, b</sup>

<figure><img src="image_77.png" /></figure>
Overview of data collection with TDS, TCATA and AEF methods

<figure><img src="image_78.png" /></figure>

During tasting, at each time

TDS
During tasting, at each time

<figure><img src="image_79.png" /></figure>

A001

<figure><img src="image_80.png" /></figure>

A005

<figure><img src="image_81.png" /></figure>

A002

<figure><img src="image_82.png" /></figure>

A006

<figure><img src="image_83.png" /></figure>

A003

<figure><img src="image_84.png" /></figure>

A007

<figure><img src="image_85.png" /></figure>

A004

<figure><img src="image_86.png" /></figure>

A008

START

STOP

TCATA
# Overview of data collection with TDS, TCATA and AEF methods

<figure><img src="image_87.png" /></figure>

During tasting, at each time

TDS

AEF-D

<figure><img src="image_88.png" /></figure>

Focus on
perceived
sensations during
tasting

<figure><img src="image_89.png" /></figure>

<figure><img src="image_90.png" /></figure>

After tasting, for each period (attack, evolution, finish)

<figure><img src="image_91.png" /></figure>

FC-AEF-D

Inspired from wine tasting
During tasting, at each time

☐ A001

☐ A005

☐ A002

■ A006

■ A003

☐ A007

■ A004

☐ A008

START

STOP

TCATA

START

Focus on
perceived
sensations during
tasting

STOP

<figure><img src="image_92.png" /></figure>

After tasting, for each period (attack, evolution, finish)

At first, I perceived

☐ A001
☐ A002

☐ A003
☐ A004

☐ A005
☐ A006

☐ A007
☐ A008

Then, I perceived

☐ A001
☐ A002

☐ A003
☐ A004

☐ A005
☐ A006

☐ A007
☐ A008

Finally, I perceived

☐ A001
☐ A002

☐ A003
☐ A004

☐ A005
☐ A006

☐ ..007
☐ A008

At first, I perceived

Free comment 1, 2

Then, I perceived

Free comment 3, 4, 1

Finally, I perceived

Free comment 3

FC-AEF-D

AEF-A
# Do low temporal resolution retrospective evaluations result in loss of information?

Objectives when designing AEF:

- **Flexible method** to measure dominance/applicability, with list of attributes or free comment (out of scope)

- Enable to **question the grain of temporal data** by comparison with TDS/TCATA

**Simplifying data collection:**

- Retrospective **task easy to understand** (no need for explanation)

- No measurement of citation times: **less heterogeneity among consumers due to delayed perceptions**

**Simplifying data analysis and interpretation:**

- **No data transformation**

- Direct visualization of the evolution of perception between periods

AEF close to conventional static evaluation methods:

- **Evaluation after tasting**

- **Loss of temporal resolution**
AEF-D vs. TDS

TDS

<figure><img src="image_93.png" /></figure>

<figure><img src="image_94.png" /></figure>

AEF-D

<figure><img src="image_95.png" /></figure>

<figure><img src="image_96.png" /></figure>

Within-subject design, 5 dark chocolates evaluated in the laboratory by 129 consumers with TDS and AEF-D
AEF-D vs. TDS

<figure><img src="image_97.png" /><figcaption>Citation rate</figcaption></figure>

<figure><img src="image_98.png" /><figcaption>AEF-D</figcaption></figure>

<figure><img src="image_99.png" /><figcaption>Citation rate</figcaption></figure>

<figure><img src="image_100.png" /></figure>

Within-subject design, 5 dark chocolates evaluated in the laboratory by 129 consumers with TDS and AEF-D

Main results:

- Same maps with CVA of durations (TDS) and CA of citations (AEF-D)

- Same dominant attributes and trajectories with both methods

- Fewer significant attributes with AEF-D

<figure><img src="image_101.png" /></figure>

AEF-D: face validity, same discrimination capacity as TDS, but slight loss of information
AEF-A vs. D-TCATA (CATA during tasting, at 3 time points)

D-TCATA

<figure><img src="image_102.png" /></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="3">P2</th></tr><tr><th>T1</th><th>T2</th><th>T3</th></tr></thead><tbody><tr><th>Alcohol</th><td>38.8</td><td>44.3*</td><td>33.3</td></tr><tr><th>Bitter</th><td>24.2</td><td>35.9*</td><td>26.7</td></tr><tr><th>Carbonated</th><td>71.1*</td><td>45.4</td><td>27.8</td></tr><tr><th>Lemon</th><td>65.9*</td><td>60.8</td><td>49.1</td></tr><tr><th>Refreshing</th><td>33.0</td><td>34.1</td><td>31.9</td></tr><tr><th>Sour</th><td>28.9</td><td>28.2</td><td>22.3</td></tr><tr><th>SweetF</th><td>13.6</td><td>13.9</td><td>13.6</td></tr><tr><th>SweetT</th><td>16.1</td><td>21.6*</td><td>18.7</td></tr></tbody></table>

AEF-A

<figure><img src="image_103.png" /></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="3">P2</th></tr><tr><th>T1</th><th>T2</th><th>T3</th></tr></thead><tbody><tr><th>Alcohol</th><td>33.3</td><td>50.0*</td><td>46.9*</td></tr><tr><th>Bitter</th><td>19.1</td><td>31.3</td><td>34.7*</td></tr><tr><th>Carbonated</th><td>68.40*</td><td>50.4</td><td>24.3</td></tr><tr><th>Lemon</th><td>60.42*</td><td>61.5*</td><td>43.4</td></tr><tr><th>Refreshing</th><td>31.3</td><td>31.6</td><td>26.7</td></tr><tr><th>Sour</th><td>32.3*</td><td>29.9</td><td>23.6</td></tr><tr><th>SweetF</th><td>19.4</td><td>17.4</td><td>17.7</td></tr><tr><th>SweetT</th><td>21.2*</td><td>18.1</td><td>16.7</td></tr></tbody></table>

Between-subject design, 4 carbonated alcoholic lemon drinks evaluated at home by 93 and 97 consumers with D-TCATA or AEF-A
AEF-A vs. D-TCATA (CATA during tasting, at 3 time points)

<figure><img src="image_104.png" /><figcaption>D-TCATA</figcaption></figure>

<table><caption>AEF-A</caption><thead><tr><th rowspan="2"></th><th colspan="3">P2</th></tr><tr><th>T1</th><th>T2</th><th>T3</th></tr></thead><tbody><tr><td>Alcohol</td><td>38.8</td><td>44.3*</td><td>33.3</td></tr><tr><td>Bitter</td><td>24.2</td><td>35.9*</td><td>26.7</td></tr><tr><td>Carbonated</td><td>71.1*</td><td>45.4</td><td>27.8</td></tr><tr><td>Lemon</td><td>65.9*</td><td>60.8</td><td>49.1</td></tr><tr><td>Refreshing</td><td>33.0</td><td>34.1</td><td>31.9</td></tr><tr><td>Sour</td><td>28.9</td><td>28.2</td><td>22.3</td></tr><tr><td>SweetF</td><td>13.6</td><td>13.9</td><td>13.6</td></tr><tr><td>SweetT</td><td>16.1</td><td>21.6*</td><td>18.7</td></tr></tbody></table>

<figure><img src="image_105.png" /></figure>

Between-subject design, 4 carbonated alcoholic lemon drinks evaluated at home by 93 and 97 consumers with D-TCATA or AEF-A

Main results:

- Similar product maps but AEF-A more discriminant

- Intra-product differences not identical (orange cells)

<figure><img src="image_106.png" /></figure>

AEF-A: face validity, better discrimination capacity compared to D-TCATA, but slightly different information
# Validity of temporal measurements

<figure><img src="image_107.png" /></figure>

ELSEVIER

Food Quality and Preference
Volume 110, August 2023, 104942

<figure><img src="image_108.png" /></figure>

Assessment of the validity and reliability of temporal sensory evaluation methods used with consumers on controlled stimuli delivered by a gustometer

Michel Visalli<sup>a, b</sup> Ⓔ, Noëlle Béna<sup>a, b</sup>, Léna Nicolle<sup>a, b</sup>, Pascal Schlich<sup>a, b</sup>
The different types of validity

Face validity

Superficial assessment of
relevance of appropriateness
of the measurement

Signal observed

"The new method is useful and complementary
to the other method"

Checked, but weak forms of validity

Criterion validity

Extent to which the
measurement compares
with an external gold
standard

Signal observed
comparable to those of a
reference method

Content validity

Does the measure cover all
the content of the
underlying construct

Is the signal observed
meaningful?

Difficult to assess because "sensory reality" is unknown

Construct validity

Correspondance with "real-life" values
Using a gustometer to deliver controlled stimuli

<figure><img src="image_109.png" /></figure>

<figure><img src="image_110.png" /></figure>

<table><thead><tr><th>Controlled variables</th><th>Measured variables</th></tr></thead><tbody><tr><td>Compound delivered</td><td>Attribute cited</td></tr><tr><td>Number of compound delivered</td><td>Number of attributes cited</td></tr><tr><td>Compound stimulation time</td><td>Attribute selection time</td></tr><tr><td>Compound stimulation duration</td><td>Attribute duration of dominance/applicability</td></tr><tr><td>Compound concentration</td><td>Attribute citation rate</td></tr><tr><td>Total stimulation duration</td><td>Time to click on STOP</td></tr></tbody></table>

Between-subject design, 14 controlled stimuli delivered by a gustometer 50 evaluated by consumers in the laboratory with TDS, TCATA or AEF-A

<figure><img src="image_111.png" /></figure>

<table><thead><tr><th>Attribute</th><th>Compound</th></tr></thead><tbody><tr><td>Acid</td><td>Citric acid</td></tr><tr><td>Salty</td><td>Sodium chloride</td></tr><tr><td>Sweet</td><td>Saccharose</td></tr><tr><td>Basil</td><td>Basil hydrosol</td></tr><tr><td>Lemon</td><td>Citral</td></tr><tr><td>Bitter</td><td>-</td></tr><tr><td>Licorice</td><td>-</td></tr><tr><td>Mint</td><td>-</td></tr></tbody></table>

Are the two concepts of dominance and applicability really different?

What about the differences observed: signal or noise?

Do citation times and durations of attributes correspond with times and durations of stimulation?

Do total durations of perception correspond to durations of stimulation?

Do citation rates correspond to sensory intensities?

<figure><img src="image_112.png" /></figure>
Validity of temporal measurements

<figure><img src="image_113.png" /><figcaption>TDS</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>TCATA</figcaption></figure>

<figure><img src="image_115.png" /><figcaption>AEF-A</figcaption></figure>

<figure><img src="image_116.png" /></figure>

<figure><img src="image_117.png" /></figure>

<figure><img src="image_118.png" /></figure>

<figure><img src="image_119.png" /></figure>

<figure><img src="image_120.png" /></figure>

<figure><img src="image_121.png" /></figure>

<figure><img src="image_122.png" /></figure>

<figure><img src="image_123.png" /></figure>

<figure><img src="image_124.png" /></figure>
Validity of temporal measurements

<figure><img src="image_125.png" /><figcaption>TDS</figcaption></figure>

<figure><img src="image_126.png" /><figcaption>TCATA</figcaption></figure>

<figure><img src="image_127.png" /><figcaption>AEF-A</figcaption></figure>

<figure><img src="image_128.png" /></figure>

<figure><img src="image_129.png" /></figure>

<figure><img src="image_130.png" /></figure>

<figure><img src="image_131.png" /></figure>

<figure><img src="image_132.png" /></figure>

<figure><img src="image_133.png" /></figure>

<figure><img src="image_134.png" /></figure>

<figure><img src="image_135.png" /></figure>

<figure><img src="image_136.png" /></figure>

Main results:

- Good correspondence between sequences of attributes (at panel level) and order of delivery of compounds
Validity of temporal measurements

<figure><img src="image_137.png" /><figcaption>TDS</figcaption></figure>

<figure><img src="image_138.png" /><figcaption>TCATA</figcaption></figure>

<figure><img src="image_139.png" /><figcaption>AEF-A</figcaption></figure>

<figure><img src="image_140.png" /></figure>

<figure><img src="image_141.png" /></figure>

<figure><img src="image_142.png" /></figure>

<figure><img src="image_143.png" /></figure>

<figure><img src="image_144.png" /></figure>

<figure><img src="image_145.png" /></figure>

<figure><img src="image_146.png" /></figure>

<figure><img src="image_147.png" /></figure>

<figure><img src="image_148.png" /></figure>

Main results:

- Good correspondence between sequences of attributes (at panel level) and order of delivery of compounds

- Citation rates of distractors between 15 and 25%, higher with applicability-based methods
Using a gustometer to deliver controlled stimuli

<figure><img src="image_149.png" /></figure>

Main results:

- Good correspondence between sequences of attributes (at panel level) and order of delivery of compounds

- Citation rates of distractors between 15 and 25%, higher with applicability-based methods

- Citation rates of attributes not always linked to concentrations of compounds despite iso-intensity, especially with TDS
# Validity of temporal measurements

<figure><img src="image_150.png" /><figcaption>TDS</figcaption></figure>

<figure><img src="image_151.png" /><figcaption>TCATA</figcaption></figure>

<figure><img src="image_152.png" /><figcaption>AEF-A</figcaption></figure>

<figure><img src="image_153.png" /></figure>

<figure><img src="image_154.png" /></figure>

<figure><img src="image_155.png" /></figure>

<figure><img src="image_156.png" /></figure>

<figure><img src="image_157.png" /></figure>

<figure><img src="image_158.png" /></figure>

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /></figure>

<figure><img src="image_161.png" /></figure>

## Main results:

- Good correspondence between sequences of attributes (at panel level) and order of delivery of compounds

- Citation rates of distractors between 15 and 25%, higher with applicability-based methods

- Citation rates of attributes not always linked to concentrations of compounds despite iso-intensity, especially with TDS

<figure><img src="image_162.png" /></figure>

Citations of both dominant and applicable attributes mostly triggered by new perceptions and familiarity/ease of identifying the sensation rather than intensity
# Validity of temporal measurements

## Correlations and differences between controlled and measured variables within method

<table><thead><tr><th rowspan="2">Variable</th><th colspan="2">TDS</th><th colspan="2">TCATA</th><th colspan="2">AEF-A</th></tr><tr><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th></tr></thead><tbody><tr><td>Nb compounds vs. nb attributes</td><td>0.80</td><td>-0.2</td><td>0.76</td><td>+0.2</td><td>0.84</td><td>0</td></tr><tr><td>Stimulation time vs. selection time</td><td>0.97</td><td>+0.9 s</td><td>0.97</td><td>+1.1 s</td><td>0.96</td><td>-</td></tr><tr><td>Stimulation duration vs. dom/app duration</td><td>0.03</td><td>-4.2 s</td><td>0.16</td><td>-1.5 s</td><td>-</td><td>-</td></tr><tr><td>Concentration vs. citation rate</td><td>0.17</td><td>-</td><td>0.32</td><td>-</td><td>0.30</td><td>-</td></tr><tr><td>Total stimulation duration vs. STOP time</td><td>0.61</td><td>+1.9 s</td><td>0.56</td><td>+2.5 s</td><td>-</td><td>-</td></tr></tbody></table>
# Validity of temporal measurements

## Correlations and differences between controlled and measured variables within method

<table><thead><tr><th rowspan="2">Variable</th><th colspan="2">TDS</th><th colspan="2">TCATA</th><th colspan="2">AEF-A</th></tr><tr><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th></tr></thead><tbody><tr><td>Nb compounds vs. nb attributes</td><td>0.80</td><td>-0.2</td><td>0.76</td><td>+0.2</td><td>0.84</td><td>0</td></tr><tr><td>Stimulation time vs. selection time</td><td>0.97</td><td>+0.9 s</td><td>0.97</td><td>+1.1 s</td><td>0.96</td><td>-</td></tr><tr><td>Stimulation duration vs. dom/app duration</td><td>0.03</td><td>-4.2 s</td><td>0.16</td><td>-1.5 s</td><td>-</td><td>-</td></tr><tr><td>Concentration vs. citation rate</td><td>0.17</td><td>-</td><td>0.32</td><td>-</td><td>0.30</td><td>-</td></tr><tr><td>Total stimulation duration vs. STOP time</td><td>0.61</td><td>+1.9 s</td><td>0.56</td><td>+2.5 s</td><td>-</td><td>-</td></tr></tbody></table>

## Main results:

- Not enough attributes reported with TDS, too many with TCATA

- Valid selection times/periods

- Dominance/applicability durations not correlated with stimulation durations (but highly correlated with citation rates)

- Citation rates poorly correlated with concentrations

- Total perception times correlated with total delivery times (but very different between methods in food products*: START/STOP only < TDS (+33%) < TCATA (+66%))
Validity of temporal measurements

Correlations and differences between same variables measured with different methods

<table><thead><tr><th rowspan="2">Variable</th><th colspan="2">TDS/TCATA</th><th colspan="2">TDS/AEF-A</th><th colspan="2">TCATA/AEF-A</th></tr><tr><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th></tr></thead><tbody><tr><td>Nb attributes</td><td>0.99</td><td>-0.4</td><td>0.97</td><td>-0.19</td><td>0.97</td><td>+0.23</td></tr><tr><td>Selection time</td><td>0.86</td><td>-0.2 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Dominance/applicability duration</td><td>0.46</td><td>-2.8 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Mean citation rate</td><td>0.64</td><td>-5.4%</td><td>0.77</td><td>+1.7%</td><td>0.77</td><td>+7.0%</td></tr><tr><td>Max citation rate</td><td>0.66</td><td>-7.4%</td><td>0.75</td><td>+0.3%</td><td>0.71</td><td>+7.6%</td></tr><tr><td>STOP time</td><td>0.95</td><td>-0.6 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
Validity of temporal measurements

Correlations and differences between same variables measured with different methods

<table><thead><tr><th rowspan="2">Variable</th><th colspan="2">TDS/TCATA</th><th colspan="2">TDS/AEF-A</th><th colspan="2">TCATA/AEF-A</th></tr><tr><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th></tr></thead><tbody><tr><td>Nb attributes</td><td>0.99</td><td>-0.4</td><td>0.97</td><td>-0.19</td><td>0.97</td><td>+0.23</td></tr><tr><td>Selection time</td><td>0.86</td><td>-0.2 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Dominance/applicability duration</td><td>0.46</td><td>-2.8 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Mean citation rate</td><td>0.64</td><td>-5.4%</td><td>0.77</td><td>+1.7%</td><td>0.77</td><td>+7.0%</td></tr><tr><td>Max citation rate</td><td>0.66</td><td>-7.4%</td><td>0.75</td><td>+0.3%</td><td>0.71</td><td>+7.6%</td></tr><tr><td>STOP time</td><td>0.95</td><td>-0.6 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Main results:

- All variables correlated, lower correlation between durations of dominance/applicability

- More attributes cited, higher citation rates, longer durations with TCATA
Correlations and differences between methods

<table><thead><tr><th rowspan="2">Variable</th><th colspan="2">TDS/TCATA</th><th colspan="2">TDS/AEF-A</th><th colspan="2">TCATA/AEF-A</th></tr><tr><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th><th>Corr.</th><th>Diff.</th></tr></thead><tbody><tr><td>Nb attributes</td><td>0.99</td><td>-0.4</td><td>0.97</td><td>-0.19</td><td>0.97</td><td>+0.23</td></tr><tr><td>Selection time</td><td>0.86</td><td>-0.2 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Dominance/applicability duration</td><td>0.46</td><td>-2.8 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Mean citation rate</td><td>0.64</td><td>-5.4%</td><td>0.77</td><td>+1.7%</td><td>0.77</td><td>+7.0%</td></tr><tr><td>Max citation rate</td><td>0.66</td><td>-7.4%</td><td>0.75</td><td>+0.3%</td><td>0.71</td><td>+7.6%</td></tr><tr><td>STOP time</td><td>0.95</td><td>-0.6 s</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Main results:

- All variables correlated, lower correlation between durations of dominance/applicability.

- More attributes cited, higher citation rates, longer durations with TCATA

All methods are appropriate to measure sequences of perceptions (order of appearance)

Citation rates only measure agreement, not intensity

What is really measured with durations is unclear

No large difference between dominance and applicability concepts

<figure><img src="image_163.png" /></figure>
# Precision and resolution of temporal measurements

<figure><img src="image_164.png" /></figure>

ELSEVIER

Food Quality and Preference

Volume 117, August 2024, 105172

<figure><img src="image_165.png" /></figure>

## Temporal precision and resolution of TDS and TCATA used with consumers

Michel Visalli<sup>a, b</sup> Ⓔ, Benjamin Mahieu<sup>c</sup>, Caroline Peltier<sup>a, b</sup>, Sylvie Cordelle<sup>a, b</sup>,

Pascal Schlich<sup>a, b</sup>
Are temporal measurements reliable?

<figure><img src="image_166.png" /></figure>

Jaccard distance computed at each time for each subject (0: no attribute in common, 1: same attributes), then averaged over subjects, then over products

<table><thead><tr><th></th><th></th><th>TDS</th><th>TCATA</th><th>AEF-D</th><th>AEF-A</th></tr></thead><tbody><tr><td rowspan="3">Agreement<br>Same products,<br>different<br>subjects</td><td>Raw time (discretization 1 s)</td><td>0.09</td><td>0.14</td><td>0.20</td><td>0.28</td></tr><tr><td>Standardized time</td><td>0.17</td><td>0.24</td><td>-</td><td>-</td></tr><tr><td>After CATA transformation</td><td>0.34</td><td>0.39</td><td>0.29</td><td>0.39</td></tr><tr><td rowspan="3">Repeatability<br>Same products,<br>same subjects</td><td>Raw time (discretization 1 s)</td><td>0.15</td><td>0.25</td><td>0.28</td><td>0.31</td></tr><tr><td>Standardized time</td><td>0.20</td><td>0.32</td><td>-</td><td>-</td></tr><tr><td>After CATA transformation</td><td>0.48</td><td>0.48</td><td>0.37</td><td>0.52</td></tr></tbody></table>

Between-subject design, 4 product categories (guacamoses, crisps, chocolates, ice teas, 4 products per category, 1 product evaluated twice) varying in sensory complexity evaluated by 68 to 75 consumers in the laboratory or at home with TDS, TCATA, AEF-D or AEF-A
# Are temporal measurements reliable?

<figure><img src="image_167.png" /></figure>

Jaccard distance computed at each time for each subject (0: no attribute in common, 1: same attributes), then averaged over subjects, then over products

<table><thead><tr><th></th><th></th><th>TDS</th><th>TCATA</th><th>AEF-D</th><th>AEF-A</th></tr></thead><tbody><tr><td rowspan="3">Agreement<br>Same products,<br>different<br>subjects</td><td>Raw time (discretization 1 s)</td><td>0.09</td><td>0.14</td><td>0.20</td><td>0.28</td></tr><tr><td>Standardized time</td><td>0.17</td><td>0.24</td><td>-</td><td>-</td></tr><tr><td>After CATA transformation</td><td>0.34</td><td>0.39</td><td>0.29</td><td>0.39</td></tr><tr><td rowspan="3">Repeatability<br>Same products,<br>same subjects</td><td>Raw time (discretization 1 s)</td><td>0.15</td><td>0.25</td><td>0.28</td><td>0.31</td></tr><tr><td>Standardized time</td><td>0.20</td><td>0.32</td><td>-</td><td>-</td></tr><tr><td>After CATA transformation</td><td>0.48</td><td>0.48</td><td>0.37</td><td>0.52</td></tr></tbody></table>

Between-subject design, 4 product categories (guacamoses, crisps, chocolates, ice teas, 4 products per category, 1 product evaluated twice) varying in sensory complexity evaluated by 68 to 75 consumers in the laboratory or at home with TDS, TCATA, AEF-D or AEF-A

## Main results:

- Agreement and repeatability very low to low

- Time standardization improve agreement and repeatability

- Temporal dimension: large source of heterogeneity (ignoring the time dimension increases agreement and repeatability by a factor of 2 or 3)

Illusory time-to-time agreement: what consequence on interpretation of curves?
What is the intra-intake temporal resolution of the measurements?

Bootstrap test to compare median selection and deselection times of attributes within a given product in order to objectify the sequentiality of perceptions

<figure><img src="image_168.png" /></figure>

Standardized time

<figure><img src="image_169.png" /></figure>

TDS, crisp C1

Standardized time
# What is the intra-intake temporal resolution of the measurements?

Bootstrap test to compare median selection and deselection times of attributes within a given product in order to objectify the sequentiality of perceptions

<figure><img src="image_170.png" /></figure>

Standardized time

<figure><img src="image_171.png" /></figure>

TDS, crisp C1

## Main results:

- Brown and light green attributes selected at the same time (not necessarily by the same subjects)

- Light blue attribute selected later than brown and light green attributes

- Light blue attribute selected twice by the subjects, the second selection occurring after the time of deselection of dark green attribute

- Some non significant attributes are cited by more than 25% of subjects but at different times (disagreement)
# What is the intra-intake temporal resolution of the measurements?

Bootstrap test to compare median selection and deselection times of attributes within a given product in order to objectify the sequentiality of perceptions

<figure><img src="image_172.png" /></figure>

<figure><img src="image_173.png" /></figure>

## Main results:

- Brown and light green attributes selected at the same time (not necessarily by the same subjects)

- Light blue attribute selected later than brown and light green attributes

- Light blue attribute selected twice by the subjects, the second selection occurring after the time of deselection of dark green attribute

- Some non significant attributes are cited by about 30% of subjects but at different times (disagreement)

<figure><img src="image_174.png" /></figure>

Usable with TDS and TCATA with no need for standardization of times (median not sensitive to outliers)

New information: overall citation rate of attributes, confidence intervals around median selection/deselection times

Test enabling absolute interpretation for all attributes, without significance line or comparison to other products
What is the between-product temporal resolution of the measurements?

<figure><img src="image_175.png" /></figure>

Reliability issue for brown attribute for C1?

If so, what about difference for brown attribute between C1 and C4?

More generally, what is the meaning of difference in citation rates?

<figure><img src="image_176.png" /></figure>
What is the between-product temporal resolution of the measurements?

Bootstrap test to compare differences in median selection and deselection times of attributes between products in order to objectify delayed perceptions

<figure><img src="image_177.png" /></figure>

Brown attribute more selected in C1 compared to C1_rep and C4 but
selected and unselected at the same times

Disagreement in overall citation rates, not in moments of perception
What is the between-product temporal resolution of the measurements?

Main results:

- Light blue attribute almost not selected in C4 while selected during the whole time for C1: difference due to applicability rather than temporality

<figure><img src="image_178.png" /></figure>
What is the between-product temporal resolution of the measurements?

<figure><img src="image_179.png" /></figure>

Main results:

- Light blue attribute almost not selected in C4 while selected during the whole time for C1: difference due to applicability rather than temporality

- Brown attribute selected later in C1 compared to C4: difference in temporality not captured by difference curves
What is the between-product temporal resolution of the measurements?

<figure><img src="image_180.png" /></figure>

<figure><img src="image_181.png" /></figure>

<figure><img src="image_182.png" /></figure>

<figure><img src="image_183.png" /></figure>

Main results:

- Light blue attribute almost not selected in C4 while selected during the whole time for C1: difference due to applicability rather than temporality

- Brown attribute selected later in C1 compared to C4: difference in temporality not captured by difference curves

- Dark blue attribute most selected in C4 compared to C1, and at different times: **difference tricky to interpret when reading difference curves**
What is the between-product temporal resolution of the measurements?

<figure><img src="image_184.png" /></figure>

TDS, crisp C1 – C4

<figure><img src="image_185.png" /></figure>

C1
78% (69-88%)

<figure><img src="image_186.png" /></figure>

C4
74% (59-88%)

C1
44% (26-62%)

<figure><img src="image_187.png" /></figure>

C4
99% (96-100%)

C1
25% (15-36%)

Main results:

- Light blue attribute almost not selected in C4 while selected during the whole time for C1: **difference due to applicability rather than temporality**

- Brown attribute selected later in C1 compared to C4: **difference in temporality not captured by difference curves**

- Dark blue attribute most selected in C4 compared to C1, and at different times: **difference tricky to interpret when reading difference curves**

<figure><img src="image_188.png" /></figure>

Temporal resolution important to consider for understanding and interpreting temporal data
Temporal resolution is about 10% of total duration: short-duration differences can be meaningless
# Product discrimination ability of temporal measurements

<figure><img src="image_189.png" /></figure>

ELSEVIER

Food Quality and Preference
Volume 115, June 2024, 105123

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Product discrimination ability of temporal sensory evaluation methods used with consumers

Michel Visalli b, c, Sylvie Cordelle b, Noëlle Béno b, Pascal Schlich b

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Now all things considered, what about discrimination?

**Citations only**

ANOVA glm

MRCA

**Citations x
time/period**

Difference curves

**Durations**

ANOVA

CVA

**Selection times**

Bootstrap

Conclusions based on evaluation of validity on controlled stimuli, on percentage of product pairs discriminated with multidimensional analysis on the 4 food product categories, and on percentage of product/attribute pairs discriminated with unidimensional analysis on the 4 food product categories
## Now all things considered, what about discrimination?

### Citations only

ANOVA glm
MRCA

- Valid measurement

- **Most discriminative and reliable**

- Same discrimination whatever the method (TDS, TCATA, AEF-D, AEF-A)

### Citations x time/period

Difference curves

- Unclear validity/interpretation

- **Discrimination gain** (applicability +40%, dominance +20%)

- Loss of reliability (TCATA, +10%)

### Durations

ANOVA
CVA

- Unclear validity

- **Short-duration differences (<3s) not captured**

- **Less discriminating than citations**

- 10% of product/attribute pairs discriminated by analysis of durations but not citations

### Selection times

Bootstrap

- Valid measurement

- **Less discriminative and reliable**

- 10% of product/attribute pairs discriminated by analysis of selection times but not citations

Conclusions based on evaluation of validity on controlled stimuli, on percentage of product pairs discriminated with multidimensional analysis on the 4 food product categories, and on percentage of product/attribute pairs discriminated with unidimensional analysis on the 4 food product categories
## Now all things considered, what about discrimination?

### Citations only

ANOVA glm
MRCA

- Valid measurement

- **Most discriminative and reliable**

- Same discrimination whatever the method (TDS, TCATA, AEF-D, AEF-A)

### Citations x
time/period

Difference curves

- Unclear validity/interpretation

- **Discrimination gain** (applicability +40%, dominance +20%)

- Loss of reliability (TCATA, +10%)

### Durations

ANOVA
CVA

- Unclear validity

- **Short-duration differences (<3s) not captured**

- **Less discriminating than citations**

- 10% of product/attribute pairs discriminated by analysis of durations but not citations

### Selection times

Bootstrap

- Valid measurement

- **Less discriminative and reliable**

- 10% of product/attribute pairs discriminated by analysis of selection times but not citations

Conclusions based on evaluation of validity on controlled stimuli, on percentage of product pairs discriminated with multidimensional analysis on the 4 food product categories, and on percentage of product/attribute pairs discriminated with unidimensional analysis on the 4 food product categories

<figure><img src="image_207.png" /></figure>

Most differences between products are not temporal

Discrimination: TCATA>AEF-A>AEF-D>TDS; Reliability: TDS>AEF-D>AEF-A>TCATA
# Discussion and recommendations

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Current Opinion in Food Science

Volume 54, December 2023, 101102

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## Use of temporal sensory evaluation methods with consumers: a position paper

Michel Visalli¹, Mara V Galmarini²,³, Pascal Schlich¹

<figure><img src="image_210.png" /></figure>
Concurrent vs. retrospective measurements

Kahneman's theory of two systems of thought (O'Brien, 2012)

System 1 (concurrent measurements)
Fast and intuitive responses

Close to perception: **time constraint stress**
→ focus on changes?
Concurrent vs. retrospective measurements

Kahneman's theory of two systems of thought (O'Brien, 2012)

System 1 (concurrent measurements)
Fast and intuitive responses

Close to perception: time constraint stress

→ focus on changes

System 2 (retrospective measurements)
Slower and deliberative responses

Rely on short term memory

→ cognition bias
Concurrent vs. retrospective measurements

Kahneman's theory of two systems of thought (O'Brien, 2012)

System 1 (concurrent measurements)
Fast and intuitive responses

Close to perception
→ focus on changes

System 2 (retrospective measurements)
Slower and deliberative responses

Rely on short term memory
→ cognition bias

As concurrent temporal methods also capture differences observed with retrospective methods (static or not), they may have a small advantage in terms of ecological validity
Does a fine grain temporal characterization really provide actionable information to formulate products or determine drivers of preferences?

<figure><img src="image_211.png" /></figure>
Dominance vs. applicability

Theoretically different concepts

Dominance

Subjective concept, no consensus definition,
but match with what consumers report (i.e.,
changes in perception)

Dominance constraint: **choice of attributes**
**perceived with the most certainty, but risk**
**of loss of information**
# Dominance vs. applicability

## Theoretically different concepts

### Dominance

Subjective concept, no consensus definition,
but match with what consumers report (i.e.,
changes in perception)

**Dominance constraint: choice of attributes perceived with the most certainty, but risk of loss of information**

### Applicability

Well defined concept

Cognitive load constraint due to maintaining the state of attributes in memory while taste working memory has limited capacity: **issue with deselection of attributes, overestimation of durations, too difficult with complex products?**
# Dominance vs. applicability

## Theoretically different concepts

### Dominance

Subjective concept, no consensus definition,
but match with what consumers report (i.e.,
changes in perception)

**Dominance constraint: choice of attributes perceived with the most certainty, but risk of loss of information**

### Applicability

Well defined concept

Cognitive load constraint due to maintaining the state of attributes in memory while taste working memory has limited capacity: issue with **deselection of attributes**, overestimation of durations, too difficult with complex products?

<figure><img src="image_212.png" /></figure>

Temporal constraint imposed on the decision-making process > choice constraint (one/several attributes) imposed by the concept: TDS and TCATA relatively similar in practice
Recommandations for method choice

<figure><img src="image_213.png" /></figure>

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Is a consumer panel the best choice for evaluating the sensory properties of a product?

Is there any benefit in using a temporal method to assess consumer perception?
Recommandations for analysis of temporal data

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Number of tests → more holistic data analyses?

<figure><img src="image_216.png" /></figure>

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Imprecise data + limited temporal resolution; difficult to separate variations due to bias, noise or signal
Qualitative data: order in which sensations are perceived; no precise quantification of perception
# Conclusion
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**Possible to measure consumers' temporal perception** with qualitative sensory analysis methods BUT **with reasonable expectations about precision and resolution**

<figure><img src="image_219.png" /></figure>

**Always important to assess data quality** (even with consumer studies)

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**No ultimate method:** every gain in temporal resolution or discrimination to the detriment of validity and/or reliability

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**Need for FAIR data** to progress in methodological and fundamental knowledge (transparency, reuse, meta-analyses)

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