foods
ArticleTDS Similarity: Outlier Analysis Using a Similarity Index toCompare Time-Series Responses of Temporal Dominance of
Sensations Tasks
Hiroharu Natsume 1,† , Shogo Okamoto 1, *,†
1

2

*
†

and Hikaru Nagano 2

Department of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan;
natsume-hiroharu@ed.tmu.ac.jp
Department of Mechanical Engineering, Kobe University, Kobe 657-8501, Japan; nagano@mech.kobe-u.ac.jp
Correspondence: okamotos@tmu.ac.jp
These authors contributed equally to this work.

Abstract: Temporal dominance of sensations (TDS) methods are used to record temporally developing
sensations while eating food samples. Results of TDS tasks are typically discussed using averages
across multiple trials and panels, and few methods have been developed to analyze differences
between individual trials. We defined a similarity index between two time-series responses of TDS
tasks. This index adopts a dynamic level to determine the importance of the timing of attributeselection. With a small dynamic level, the index focuses on the duration for attributes to be selected
rather than on the timing of the attribute selection. With a large dynamic level, the index focuseson the temporal similarity between two TDS tasks. We performed an outlier analysis based on thedeveloped similarity index using the results of TDS tasks performed in an earlier study. Certain
samples were categorized as outliers irrespective of the dynamic level, whereas the categorizationof a few samples depended on the level. The similarity index developed in this study achieved
individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to
TDS methods.

Citation: Natsume, H.; Okamoto, S.;

Keywords: temporal dominance of sensations; individual analysis; similarity index; discrete
time; strawberry

Nagano, H. TDS Similarity: Outlier
Analysis Using a Similarity Index toCompare Time-Series Responses ofTemporal Dominance of Sensations
Tasks. Foods 2023, 12, 2025. https://doi.org/10.3390/foods12102025
Academic Editor: Jean-Xavier
Guinard
Received: 16 March 2023
Revised: 22 April 2023
Accepted: 12 May 2023
Published: 17 May 2023

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
The temporal dominance of sensations (TDS) method [1,2] enables us to record the temporal evolution of multiple types of sensations, including gustatory, olfactory, and textural
feelings while eating food samples. Such time evolution is caused by the physical, chemical,
and thermal changes of foods in the mouth, part of which is brought about by saliva [3] andmouth and tongue motion [4], temporal changes in sensory responses including adaptation
at peripheral and central nervous levels [5], and cross-modal interactions [6–9]. For example, the habituation of olfactory sensations is prominent in humans [5,10] and may affectthe time-series responses while eating foods. TDS methods have garnered attention infood science since the 2010s because they are more cost-effective than previous time-series
sensory evaluation methods such as the time-intensity method [11,12]. The propertiesof the data acquired through TDS methods and analysis techniques for TDS methods
continue to be intensively studied by many researchers [13–15]. TDS methods are largely
used for food and beverages; however, recent studies have applied the method to othertypes of stimuli and modalities. Examples include audio [16], visual [17], audiovisual [18],and haptic stimuli [19,20]. Furthermore, there exist modified TDS methods, including the
temporal check-all-that-apply method [21,22]. In addition, the TDS method can be used toevaluate temporal and overall food liking or preferences [23,24] and to investigate theirtemporal drivers.

Foods 2023, 12, 2025. https://doi.org/10.3390/foods12102025

https://www.mdpi.com/journal/foods

Foods 2023, 12, 2025

2 of 11

The TDS method is primarily used to determine the tendency of entire panels, and theaverage of multiple trials from all panels is often discussed. For example, the TDS curve [2],a typical analysis and visualization method for the TDS method, plots the proportion of thepanels that select each sensory attribute as dominant over time. Such proportions are calleddominance proportions. Inevitably, other analysis methods based on TDS curves, including
sensory trajectories [25] and casual analyses [26,27], deal with the average response among
the panels as well. These average responses are influenced by outlier samples; however,methods to detect outliers have not been developed for TDS methods. Meyners et al.
proposed a method to test whether a panel is statistically different from the other panels
using a randomization test [28]. Lepage et al. developed a method to test whether panels
differ from each other in their sensory responses and abilities to distinguish food products
based on dominance durations that are time periods for descriptive attributes to be selected
as dominant [29]. These methods assume that a panel repeatedly evaluates the same
foods. Therefore, the methods developed by Meyners [28] and Lepage et al. [29] cannot
be applied to individual TDS trials. It is possible to simulate time-series responses of
TDS tasks with semi-Markov chains [30–32]. Cardot et al. [30] classified the whole panel
into several semi-Markov processes according to the characteristics of their responses.
However, this corresponded to a classification analysis of panels, and was not intended
to discuss individual TDS trials. Individual differences and effects of mental states exist
in food preferences and perception [33–37]; hence, a method for analyzing differencesamong multiple TDS trials is required. However, to date no method has been proposed toinvestigate the similarity or dissimilarity between different TDS trials.
Owing to the quantitative similarities or dissimilarities between multiple TDS trials,
several analytical methods that require distance scales between samples can be developed.
Representative examples of such methods include outlier analysis and hierarchical clustering. In this study, we developed an index to evaluate the similarity between two TDS trials.
This index compares the time series responses of two trials discretized into R intervals
using the Euclidean distance. The value of R is the level of dynamics, and indicates theextent to which the temporal aspect should be considered necessary. For small R values,
the focus is on the similarity of attribute selection during the trial periods between two
trials rather than on the temporal similarity between the trials. We applied this index to theresult of TDS tasks on strawberries [38] and examined how the similarity index depends
on R.
As an essential application of the similarity index, we performed an outlier analysisof TDS tasks for strawberries. To the best of our knowledge, outlier analysis methods
have not yet been developed for TDS methods. Even well-controlled experiments cannot avoid outliers in samples. Our study can provide the TDS method with a common
data-preprocessing method to detect outliers. The present study is an extension of an
earlier study [39] in which the concept of dissimilarity between TDS trials was introduced;
however, the variable R was not used. In the present study, the effects of the R values areintensively investigated.
2. TDS Method and Task
In this section, the typical task of the TDS method and the calculation of dominanceproportions are introduced. More information can be acquired from [1,13]. As shown in
Figure 1, the tasks of the TDS method are performed using a graphical user interface on acomputer. The interface has a maximum of 10–12 buttons with labels of attribute words
representing tastes, odors, and mouth feels. In addition, there are two buttons to startand stop the recording. When a panel performs a task, the participant places the foodsample in their mouth and simultaneously presses the start button. The panel then selects
buttons with attribute words. These buttons correspond to the most dominant sensationthat the panel experiences at each moment. Multiple buttons cannot be selected at thesame time. After selecting one attribute button, that button is kept selected until anotherbutton is selected. The same attribute words can be selected more than one time or never.

Foods 2023, 12, 2025

3 of 11When the food is swallowed, the panel presses the stop button, and the task is terminated.
The computer application records the time when the panel selects the buttons. Individual
panels repeat the tasks for the same food sample several times.

Figure 1. Graphical user interface used for recording TDS tasks. In the software shown here,
the START button turns into the STOP button after it is pressed.

The result of an example TDS task is shown in Figure 2. The response to attribute( j)

i in the jth TDS trial is expressed by a binary function xi (t) ∈ {0, 1} of continuous
( j)

( j)

time t. When attribute i is selected at t in jth trial, xi (t) = 1; otherwise, xi (t) = 0.
The continuous time t is the normalized time ranging from 0 to 1. The commencement andend of the individual task correspond to t = 0 and t = 1, respectively.

( j)

Figure 2. Result of a TDS task as binary functions xi (t). None is 1 only when no attribute is selected
immediately after the start button is pressed. At any moment, only one attribute takes the value 1.
( j)

We use an additional function x0 (t) ∈ {0, 1}, which equals 1 only before the first
attribute button is selected. In other words, after a certain button has been selected, it
remains 0 until the end. Hence, at each moment t, the following equation holdsq∑ xi (t) = 1,

i =0

( j)

(1)

Foods 2023, 12, 2025

4 of 11

where q is the number of attributes used in the TDS task. Multiple trials are averaged using
the following equation:pi ( t ) =1 n ( j)x i ( t ),
n j∑
=1

(2)

where n is the number of trials (the number of panels × repetitions) and pi (t) represents the
proportion with which attribute i is selected at t among all the trials. After being smoothed
over time, this proportion is referred to as a dominance proportion [1].
3. Similarity Index for TDS Trials
A method for calculating the similarity of the results of two arbitrary TDS tasks is
described in this section. The results of the TDS tasks, which are continuous functions, areconverted to discrete functions before computing the similarity. This process follows( j)

Xi [ k ] = R

Z (k+1)/R
k/R

( j)

xi (t)dt,

(3)
( j)

where R is the number of discretized time intervals and Xi [k] is the average value of( j)

xi on the kth discretization interval (k = 0, 1, . . . , R − 1), is equivalent to the dominanceduration in the kth interval. The interval used to calculate the dominance durations is 1/R.
( j)
When R = 1, Xi [k] has the value only for k = 0, which represents the dominance duration
for the entire period of the trial. This conversion from continuous to discrete forms can beapplied to the dominance proportion pi (t) as well, which is expressed as Pi [k ].
We defined the similarity index S between two TDS trials a and b with a certain
R value:vu
R −1 u q
2
1
( a)
(b)
t
S R ( X ( a) , X (b) ) = 1 − √Xi [ k ] − Xi [ k ] .
(4)∑∑
2R k=0 i=0The second term of (4) corresponds to the average Euclidean distancein a (q + 1)-dimensional√
space between TDS trials a and b, which is normalized by 2R such that its maximum( a)value is 1. In the (q + 1)-dimensional space, the coordinates of the ith axis are Xi [k ] and(b)

Xi [k ]. The distance corresponds to the dissimilarity of the two trials. The similarity is thecomplement of the dissimilarity for the 1.
In addition, SR can be applied to the dominance proportion P. For example, SR ( X (a) , P)
indicates the similarity of trial X (a) to the centroid of entire trials. We defined the similarity
between the two trials; however, the dissimilarity or distance in the second term of (4) may
be preferable for different analysis purposes. Certain aspects of the above computational
process are similar to the method proposed by Pineau et al. for hypothesis testing of two
sets of TDS curves [13].
4. TDS Data of Strawberries
We used the data from TDS tasks collected by Shimaoka et al. [38]. They conducted the
TDS tasks for strawberries by involving seventeen paid university student panels (fourteen
males and three females) in their 20s. They were not experts in the sensory evaluation offoods, and familiarized themselves with the TDS tasks, including how to use graphical userinterfaces, before the experiment for several minutes until they felt confident. Individual
panels replicated three TDS tasks in which one strawberry was eaten in each task. In total,
51 tasks were conducted (n = 51). The participants used eight types of attributes (q = 8)
selected by ten participants through a vote among 218 candidate attributes. The eight
attributes were sweet, sour, fruity, green, watery, juicy, aromatic, and light.

Foods 2023, 12, 2025

5 of 11

Figure 3 shows the dominance proportions calculated from all the tasks. Typically,in the earlier phase, sweet, juicy, and fruity were dominantly selected. The attribute aromatic
was more prominent in the middle phase than in the other phases. Further, sour was
frequently selected in the middle phase, and remained dominant in the last period. Similar
results were reported in another study of TDS tasks with strawberries [26].

Figure 3. TDS curve: time-series of the average of all trials. This curve is called the TDS curve. The
dominance proportion (the vertical axis) is the proportion of trials in which an attribute word wasselected at a given time. Adapted from [38].

5. Similarity Index with Different R Values: Example of Strawberries
The similarity index varies with R. In particular, the index is almost different between
small R values (such as 1–3) and large R values (such as 100). Here, using strawberries
as an example, we investigated how the similarity indices are different between different
R values. We calculated the correlation coefficients between the similarity index values
between different R values to obtain the influences of R values on similarity scores.
We assumed
n the strawberry data in the previous
o section to be a set of continuous( j)functions for t: xi (t) | j = 1, 2, . . . , 51; i = 0, 1, . . . , 8 , where 51 and 8 are the numbers of
TDS tasks or replications and attributes, as used in the previous experiment [38]. As in (2),pi (t) is the average of all trials or dominance proportions. For given values of R, CR is theset of the values of SR for each trial and pi (t):noCR = SR ( X ( j) , P) | j = 1, 2, . . . , 51 ,(5)
where X ( j) and P are the symbols of the jth TDS trial and mean dominance proportions,
respectively. When R = 1, the dominance durations of two trials were compared without considering the temporality. When R = 3, the similarity was computed for each of thethree intervals, that is, the early, middle, and last phases of the task, and the temporalityof the dominance proportions was moderately considered. When R = 10, provided thatthe average eating period of a strawberry was about 30 s [38], two pairs of trials werecompared at intervals of approximately 3 s. The correlation coefficient between two sets
CR1 and CR2 is defined as
51 ∑ CR1 − C R1

r R1,R2 =

j =1

( j)



( j)

CR2 − C R2σR1 σR2

(6)

where σR1 and σR2 are the standard deviations of CR1 and CR2 , respectively, and C R1 andC R2 are the mean values of CR1 and CR2 , respectively.
The r R1,R2 values for ( R1, R2) ∈ {1, 3, 10, 30, 50, 100}2 are shown in Table 1. When thevalues of R1 and R2 were close, the two sets CR1 and CR2 are correlated more strongly.
For example, the indices for R1 = 1 and R2 = 3 are strongly correlated with r1,3 = 0.797;

Foods 2023, 12, 2025

6 of 11

however, those for R1 = 1 and R2 = 100 are only weakly correlated with r1,100 = 0.251.
Figure 4 is the graphical version of Table 1, and shows r R1,R2 values with R2 = {1, 5, 20, 100}and R1 varying from 1 to 100. Similar to Table 1, the correlation coefficient between CR1
and CR2 is higher when R1 and R2 are closer to each other. When R2 = 20, the correlation
coefficient is greater than 0.5 for any R1, indicating that the similarity values for R = 20 arelargely or moderately correlated with those for the other R values.

Figure 4. r R1,R2 : Correlation coefficient of similarities between CR1 and CR2 . The numbers of thediscretized intervals are R1 = {1, 2, . . . , 100} and R2 = {1, 5, 20, 100}.
Table 1. Correlation coefficients r R1,R2 with different R1 and R2 values. R1 and R2 are the numbersof discretization.
R2

R1

1
3
10
30
50
100

1

3

10

30

50

100

1.000
0.797
0.675
0.521
0.385
0.251

1.000
0.826
0.594
0.429
0.269

1.000
0.848
0.693
0.548

1.000
0.941
0.865

1.000
0.972

1.000

6. Outlier Detection
6.1. Methods
To test the normality of the value distribution in CR , we applied the Kolmogorov–
Smirnov normality test to CR . When the hypothesized normality was not denied, we judged
potential outliers using the range defined by the mean and standard deviation. Trials, thatis, members in CR , were considered potential outliers if they did not fall within the range of[µ − 1.64σ, +∞], where µ is the sample mean and σ is the sample standard deviation calculated among all the values in CR . This range contains 95% of all the samples, the remaining
5% with small similarity values being screened out. The range was [µ − 2.33σ, +∞] forconsidering 99% of the samples. Note that the upper side of the distribution does notneed to be considered because trials with high similarity represent the population well.
When the samples in CR are judged to not be subjected to the normal distribution, outlier
detection methods should be selected depending on the type of distribution. Typically,a method using box plots and interquartile range can be used for determining outliers
among non-normally distributed samples.
If the mean is calculated across all samples, it can be biased by potential outliers.
To avoid such cases, a method based on the minimum covariance determinant [40] may
be used.

Foods 2023, 12, 2025

7 of 116.2. Example of Strawberries
We performed an outlier analysis on the TDS data for strawberries for eachR = {1, 3, 10, 30, 50, 100}. Figure 5 shows the distribution of the similarity indices between the individual trials and the average, that is, the TDS curves, for each R value.
The p-value of the Kolmogorov–Smirnov test was greater than 0.05 for all R values; hence,
we assumed the normal distribution to detect potential outliers. The detected outliers areshown in Table 2 as “+”. Each trial is identified by the panel ID of an alphabetic letter andthe iteration count of the task. Trial C3, the third trial of panel C, was classified as an outlier
for any R value, while other trials were classified as such only for small, large, or medium
R values. For example, N1, that is, the first trial of panel N, was categorized as a potential
outlier only when R = 1. The time series for each trial are shown in Figure 6.

Figure 5.
Distribution of similarities between individual trials and the average (P) for
R = 1, 3, 10, 30, 50, and 100. The width of each bin is 0.025. For all R values, the Kolmogorov–
Smirnov test did not reject normality with p < 0.05.

Figure 6. Time-series of potential outliers. Each box with an attribute label represents the durationand timing for which the attribute was selected. “NO SELECTION” means that no attribute wasselected during these periods.

Foods 2023, 12, 2025

8 of 11

Table 2. Detected outliers with different R values; + indicates outliers. Trial IDs consist of analphabetic letter representing a panel and the iteration count of the trial.
R
1

Trial ID

N1
E1
C2
C3

3

10

30

+

+

+

+

50

100

+
+

+

+

+

+

6.3. Discussion: Semantic Validity of Outliers
Here, we discuss or interpret the reasons for certain trials being judged as outliers in theanalysis in Section 6.2, which corresponds to the semantic validation of the outlier analysis.
In trial N1, sour was selected for almost the complete duration of the task. As shownin Figure 3, on average sour was the most dominant attribute in the second half of the task.
Nevertheless, N1 was classified as an outlier when R = 1. This was because the numberof attributes selected during the task was too small in trial N1. On average, several types
of attributes, such as sweet, juicy, watery, and sour, were selected for sustained periods;
however, in trial N1 the dominance durations of all attributes except for sour and juicy
were 0, which distinguishes this trial from the average trial.
E1 was classified as an outlier at medium R values. In this trial, attributes thatexhibited low proportions in Figure 3 were selected over the entire period, such as wateryand aromatic. In average trials, watery was largely selected in the early phase of the task,
as shown in Figure 3. However, in trial E1 watery was selected even in the middle andlast parts of the task. This trial was not considered an outlier when focusing on morechronological details with large R values, such as 50 and 100.
In attribute selection, C2 and C3 exhibited similar patterns. However, C2 was classified
as an outlier only when R = 50, whereas C3 was considered an outlier for every R value,
as shown in Table 2. For R = 30 and 100, C2 exhibited small similarity values; however,they were barely within the range of the inliers. In both trials, aromatic was selected in
the later period, in which aromatic was not prominent on average. In the early period,
however, typical attributes such as juicy and f ruity were selected in both C2 and C3. In C3,
juicy and f ruity were selected earlier than in the average trial, as shown in Figure 3. Hence,
C3 was judged as an outlier with large R values, with which the selected attributes werechronically compared. Further, in C3, sour, which was selected in the majority of the trials,
was not selected at all. This caused C3 to be judged as an outlier with small R, in which
dominance durations are more important than timing.
As aforementioned, four trials that were detected as outliers with at least one R value
seem distinct from the typical panel behavior shown in Figure 3. In the above outlier
analysis, different values of R led to different outliers. This means that the developed
similarity index changes the level of dynamics considered depending on the R value.
7. General Discussion
A key feature of the developed similarity index is that it can change the degree ofemphasis on timing depending on the value of R. We assume that an appropriate R value
depends on the research target, which varies according to the purpose of the analysis andthe type of foods to be tested. For example, a low R value may be used for foods withlarge individual differences in eating time, such as chewy foods. For such foods, closely
focusing on time-series is not meaningful. In contrast, a large R value may be preferred if
researchers wish to conduct a detailed investigation of the temporal changes of sensations.
As a systematic method, we propose setting R based on the correlation coefficients of thesimilarity values between the different R values. In the example of strawberries, as shownin Figure 4, the correlation coefficients between the index values at R = 20 and those at theother R values ranging from 1–100 exhibit constant high values. Such R (in this case, 20) isFoods 2023, 12, 2025

9 of 11

recommendable because it is in accordance with both small R values, where the timings
of attribute selection are not deemed important, and large R values, where the timings
are considered important. For strawberries, the average task duration was approximately
30 s [38]. Hence, for R = 20, one interval corresponds to about 1.5 s. Previous studies havediscussed TDS curves by dividing the entire trial period into three or four equal parts; inother words, they used R = 3 [13,29] or R = 4 [41]. However, these studies not provide
suggestions on the question of how many intervals the entire period should be split into.
One notable criterion for determining the R value was suggested in [26], where continuous
TDS curves were discretized with an interval of approximately 1 s for causality analysis.
The authors referred to the minimum interval between two successive button selections
during the TDS tasks. The panels largely switched attributes in more than 1 s. This can be areasonable method for determining R. It may not be beneficial to discuss and analyze thebehaviors of panels when the temporal resolution is smaller than the minimum behavioral
response time. Another perspective on how to set an appropriate R value involves dummy
outliers. For example, in our case several TDS samples of non-strawberry foods could havebeen mixed with those for strawberries. The samples of non-strawberry foods could thenbe perfectly determined as outliers, with the dummy TDS samples used for performance
evaluation of the outlier analysis.
In this study, only strawberries were used as food samples. Therefore, at present
the nature of the index discussed above only holds for strawberries. Furthermore, the data
used in this study were collected from panels with a limited background. The generalizability of the index must be confirmed by further testing with a wider variety of foods
and panels. To compute the similarity between trials, we used the Euclidean distance. Other
popular distance measures include the Manhattan distance and distances based on probability.
The effect of the choice of distance scale on the similarity index should be further studied. The
similarity index developed in this study treats all attributes as having the same importance.
However, it may be practical to use only essential attributes or to weight important attributes inorder to compute similarity. Moreover, as demonstrated in this study, outlier analysis based on
the developed similarity index can detect potentially outlying samples. However, it does notshow how such samples differ from typical samples. Thus, a supplementary method may bedesirable to help determine whether samples detected as outliers should be removed.
8. Conclusions
In the TDS method, the average of all trials is discussed generally, and few analyses ofindividual trials are conducted. Therefore, in this paper we have proposed a similarity index
that can be used to compare individual TDS trials. This index can adjust the importanceof the temporal similarity by changing the R parameter, which represents the number of
discretizations. Using this index, we performed an outlier analysis for TDS on strawberry
panel data. Different trials were classified as outliers with and without an emphasis ontime series. These trials were confirmed to be vastly different from the average trial. Outlier
analyses can be employed in all experiments using the TDS method to exclude outliers that
substantially bias the average consumer responses. In addition, this index can be useful forother individual analyses that use distance measures, such as hierarchical clustering [42].
Challenges to be solved in the future include determining the appropriate R value fordifferent analyses. The method used for outlier analysis can be improved in the future as
well. Currently, it does not indicate the reasons behind certain samples being judged as
outliers; thus, a method that is better able to explain these reasons is required.
Author Contributions: Conceptualization, H.N. (Hiroharu Natsume), S.O. and H.N. (Hikaru Nagano);
Methodology, H.N. (Hiroharu Natsume); Software, H.N. (Hiroharu Natsume); Validation,
H.N. (Hiroharu Natsume) and H.N. (Hikaru Nagano); Formal analysis, H.N. (Hiroharu Natsume);
Investigation, S.O.; Writing—original draft, H.N. (Hiroharu Natsume) and S.O.; Writing—review andediting, H.N. (Hiroharu Natsume), S.O., and H.N. (Hikaru Nagano); Visualization,
H.N. (Hiroharu Natsume); Supervision, S.O.; Project administration, S.O.; Funding acquisition, S.O.
All authors have read and agreed to the published version of the manuscript.

Foods 2023, 12, 2025

10 of 11

Funding: Lotte Foundation, Grant Number 2023-B-1.
Data Availability Statement: Data is contained within the article.
Conflicts of Interest: The authors declare no conflict of interest.

References
1.
2.

3.
4.
5.
6.
7.
8.
9.

10.
11.
12.
13.

14.
15.
16.
17.

18.
19.
20.

21.
22.
23.
24.
25.

ISO 13299; Sensory Analysis–Methodology–General Guidance for Establishing a Sensory Profile. International Organization forStandarzation: Geneva, Switzerland, 2016.
Pineau, N.; Schlich, P.; Cordelle, S.; Mathonniére, C.; Issanchou, S.; Imbert, A.; Rogeaux, M.; Etiévant, P.; Köster, E. Temporal
dominance of sensations: Construction of the TDS curves and comparison with time-intensity. Food Qual. Prefer. 2009, 20, 450–455.
[CrossRef]
Running, C.A. Oral sensations and secretions. Physiol. Behav. 2018, 193, 234–237. [CrossRef] [PubMed]
Green, B.G.; Nachtigal, D. Somatosensory factors in taste perception: Effects of active tasting and solution temperature. Physiol.
Behav. 2012, 107, 488–495. [CrossRef] [PubMed]
Pellegrino, R.; Sinding, C.; de Wijk, R.; Hummel, T. Habituation and adaptation to odors in humans. Physiol. Behav. 2017,
177, 13–19. [CrossRef] [PubMed]
Thomas-Danguin, T.; Guichard, E.; Salles, C. Cross-modal interactions as a strategy to enhance salty taste and to maintain likingof low-salt food: A review. Food Funct. 2019, 10, 5269–5281. [CrossRef] [PubMed]
Wang, G.; Bakke, A.J.; Hayes, J.E.; Hopfer, H. Demonstrating cross-modal enhancement in a real food with a modified ABX test.
Food Qual. Prefer. 2019, 77, 206–213. [CrossRef]
Greis, M.; Kukkonen, R.; Lampi, A.M.; Seppä, L.; Partanen, R.; Sandell, M. The Impact of Vanilla and Lemon Aromas on Sensory
Perception in Plant-Based Yogurts Measured with Static and Dynamic Methods. Foods 2022, 11, 2030. [CrossRef]
Kakutani, Y.; Narumi, T.; Kobayakawa, T.; Kawai, T.; Kusakabe, Y.; Kunieda, S.; Wada, Y. Taste of breath: The temporal orderof taste and smell synchronized with breathing as a determinant for taste and olfactory integration. Sci. Rep. 2017, 7, 8922.
[CrossRef]
Kim, K.; Bae, J.; Jin, Y.; Moon, C. Odor habituation can modulate very early olfactory event-related potential. Sci. Rep. 2020,
10, 18117. [CrossRef]
Lee, W.; Pangborn, M.W. Time-intensity: The temporal aspects of sensory perception. Food Technol. 1986, 40, 71–78.
Duizer, L.; Gullett, E.; Findlay, C. Time-intensity methodology for beef tenderness perception. J. Food Sci. 1993, 58, 943–947.
[CrossRef]
Pineau, N.; Schilch, P. 13—Temporal dominance of sensations (TDS) as a sensory profiling technique. In Rapid Sensory ProfilingTechniques; Delarue, J., Lawlor, J.B., Rogeaux, M., Eds.; Woodhead Publishing Series in Food Science, Technology and Nutrition;
Woodhead Publishing: Sawston, UK, 2015; pp. 269–306. [CrossRef]
Di Monaco, R.; Su, C.; Masi, P.; Cavella, S. Temporal dominance of sensations: A review. Trends Food Sci. Technol. 2014, 38, 104–112.
[CrossRef]
Schlich, P. Temporal dominance of sensations (TDS): A new deal for temporal sensory analysis. Curr. Opin. Food Sci. 2017,15, 38–42. [CrossRef]
Kantono, K.; Hamid, N.; Shepherd, D.; Lin, Y.H.T.; Skiredj, S.; Carr, B.T. Emotional and electrophysiological measures correlate toflavour perception in the presence of music. Physiol. Behav. 2019, 199, 154–164. [CrossRef] [PubMed]
Merlo, T.C.; Soletti, I.; Saldaña, E.; Menegali, B.S.; Martins, M.M.; Teixeira, A.C.B.; dos Santos Harada-Padermo, S.; Dargelio, M.D.;
Contreras-Castillo, C.J. Measuring dynamics of emotions evoked by the packaging colour of hamburgers11The word hamburger
is defined as a sandwich consisting of a cooked patty of ground or chopped beef, usually in a roll or bun, variously garnished.
using Temporal Dominance of Emotions (TDE). Food Res. Int. 2019, 124, 147–155. [CrossRef] [PubMed]
Peltier, C.; Visalli, M.; Thomas, A. Using temporal dominance of emotions at home. Impact of coffee advertisements on consumers’
behavior and methodological perspectives. Food Qual. Prefer. 2019, 71, 311–319. [CrossRef]
Boinbaser, L.; Parente, M.E.; Castura, J.C.; Ares, G. Dynamic sensory characterization of cosmetic creams during application
using Temporal Check-All-That-Apply (TCATA) questions. Food Qual. Prefer. 2015, 45, 33–40. [CrossRef]
Nagano, H.; Saito, N.; Matsumori, K.; Kazama, T.; Konyo, M.; Yokokohji, Y. On the Analysis of Tactile Sensation Based on Time
Measurement: An Experimental Case Study on the Interaction Between Skin and Lotion. IEEE Trans. Haptics 2023, 16, 1–6 .
[CrossRef]
Castura, J.C.; Antúnez, L.; Giménez, A.; Ares, G. Temporal Check-All-That-Apply (TCATA): A novel dynamic method forcharacterizing products. Food Qual. Prefer. 2016, 47, 79–90. [CrossRef]
Castura, J.; Rutledge, D.; Ross, C.; Næs, T. Discriminability and uncertainty in principal component analysis (PCA) of temporalcheck-all-that-apply (TCATA) data. Food Qual. Prefer. 2022, 96, 104370. [CrossRef]
Thomas, A.; Visalli, M.; Cordelle, S.; Schlich, P. Temporal Drivers of Liking. Food Qual. Prefer. 2015, 40, 365–375. [CrossRef]
Greis, M.; Sainio, T.; Katina, K.; Kinchla, A.J.; Nolden, A.; Partanen, R.; Seppä, L. Dynamic texture perception in plant-based
yogurt alternatives: Identifying temporal drivers of liking by TDS. Food Qual. Prefer. 2020, 86, 104019. [CrossRef]
Lenfant, F.; Loret, C.; Pineau, N.; Hartmann, C.; Martin, N. Perception of oral food breakdown. The concept of sensory trajectory.
Appetite 2009, 52, 659–667. [CrossRef]Foods 2023, 12, 2025

26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.

11 of 11

Okada, T.; Okamoto, S.; Yamada, Y. Affective dynamics: Causality modeling of temporally evolving perceptual and affective
responses. IEEE Trans. Affect. Comput. 2022, 13, 628–639. [CrossRef]
Okamoto, S. Structural modeling of temporal dominance responses using covariances of contemporary changes in subjective
qualities. Int. J. Affect. Eng. 2021, 20, 127–130. [CrossRef]
Meyners, M. Panel and panelist agreement for product comparisons in studies of temporal dominance of sensations. Food Qual.
Prefer. 2011, 22, 365–370. [CrossRef]
Lepage, M.; Neville, T.; Rytz, A.; Schlich, P.; Martin, N.; Pineau, N. Panel performance for temporal dominance of sensations.
Food Qual. Prefer. 2014, 38, 24–29. [CrossRef]
Cardot, H.; Lecuelle, G.; Schlich, P.; Visalli, M. Estimating finite mixtures of semi-Markov chains: An application to thesegmentation of temporal sensory data. J. R. Stat. Soc. Ser. C R. Stat. Soc. 2019, 68, 1281–1303. [CrossRef]
Lecuelle, G.; Visalli, M.; Cardot, H.; Schlich, P. Modeling temporal dominance of sensations with semi-Markov chains. Food Qual.
Prefer. 2018, 67, 59–66. [CrossRef]
Okamoto, S. Bootstrap resampling of temporal dominance of sensations curves to compute uncertainties. Foods 2021, 10, 2472.
[CrossRef]
Puputti, S.; Aisala, H.; Hoppu, U.; Sandell, M. Multidimensional measurement of individual differences in taste perception. Food
Qual. Prefer. 2018, 65, 10–17. [CrossRef]
Roukka, S.; Puputti, S.; Aisala, H.; Hoppu, U.; Seppä, L.; Sandell, M.A. The individual differences in the perception of oral
chemesthesis are linked to taste sensitivity. Foods 2021, 10, 2730. [CrossRef] [PubMed]
Norton, V.; Lignou, S.; Methven, L. Influence of age and individual differences on mouthfeel perception of whey protein-fortified
products: A review. Foods 2021, 10, 433. [CrossRef]
Hyldelund, N.B.; Frederiksen, C.; Byrne, D.V.; Andersen, B.V. Is stress taking the pleasure out of food?—A characterization of thefood pleasure profiles, appetite, and eating behaviors of people with chronic stress. Foods 2022, 11, 1980. [CrossRef]
Lin, Y.H.T.; Hamid, N.; Shepherd, D.; Kantono, K.; Spence, C. Musical and non-musical sounds influence the flavour perceptionof chocolate ice cream and emotional responses. Foods 2022, 11, 1784. [CrossRef] [PubMed]
Shimaoka, N.; Okamoto, S.; Akiyama, Y.; Yamada, Y. Linking temporal dominance of sensations for primary-sensory andmulti-sensory attributes using canonical correlation analysis. Foods 2022, 11, 781. [CrossRef]
Natsume, H.; Okamoto, S.; Shimaoka, N. Outlier analysis in time-evolving sensory evaluation tasks. In Proceedings of the Global
Conference on Life Sciences and Technologies, Osaka, Japan, 7–9 March 2022; pp. 212–213. [CrossRef]
Rousseeuw, P.J.; Leroy, A.M. Robust Regression and Outlier Detection; Wiley Series in Probability and Statistics; John Wiley & Sons,
Inc.: Hoboken, NJ, USA, 1987. [CrossRef]
Meyners, M. Temporal liking and CATA analysis of TDS data on flavored fresh cheese. Food Qual. Prefer. 2016, 47, 101–108.
[CrossRef]
Natsume, H.; Okamoto, S.; Nagano, H. Hierarchical clustering analysis of the temporal dominance of sensations tasks. In
Proceedings of the International Symposium on Affective Science and Engineering, Online, 8 March 2023; p. C00012.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.