foods
ArticleLinking Temporal Dominance of Sensations forPrimary-Sensory and Multi-Sensory Attributes Using CanonicalCorrelation Analysis
Nanako Shimaoka 1,† , Shogo Okamoto 2,3, *,† , Yasuhiro Akiyama 3 and Yoji Yamada 3
1

2
3

*
†

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Citation: 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. https://doi.org/
10.3390/foods11060781
Academic Editor: Herbert L.
Meiselman
Received: 1 February 2022

Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan;
shimaoka.nanako@h.mbox.nagoya-u.ac.jp
Department of Computer Sciences, Tokyo Metropolitan University, Hino 191-0065, Japan
Department of Mechanical Systems Engineering, Nagoya University, Nagoya 464-8601, Japan;
yasuhiro.akiyama@mae.nagoya-u.ac.jp (Y.A.); yoji.yamada@mae.nagoya-u.ac.jp (Y.Y.)
Correspondence: okamotos@tmu.ac.jp
These authors contributed equally to this work.

Abstract: Sensory responses dynamically change while eating foods. Temporal dominance ofsensations (TDS) methods record temporal evolution and have attracted attention in the last decade.
ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials.
However, only a few studies have attempted to link the dynamics of two different levels of sensory
attributes. We propose a method to link the concurrent values of dominance proportions for primaryand multi-sensory attributes using canonical correlation analysis. First, panels categorized several
attributes into primary- and multi-sensory attributes. Primary-sensory attributes included sweet,
sour, fruity, green, watery, juicy, aromatic, and light. Multi-sensory attributes included refreshing,
fresh, pleasurable, rich/deep, ripe, and mild. We applied the TDS methods to strawberries using thesetwo categories of attributes. The obtained canonical correlation model reasonably represented therelationship between the sensations in a reductive manner using five latent variables. The latentvariables couple multiple primary- and multi-sensory responses that covary. Hence, the latentvariables suggest key components to comprehend food intake experiences. We further compared themodel based on the dominance proportions and the time-derivatives of the dominance proportions.
We found that the former model was better in terms of the ease of interpreting the canonical variables
and the degree to which the canonical variables explain the dominance proportions. Thus, thesemodels help understand and leverage the sensory values of food products.
Keywords: sensations; canonical correlation analysis; strawberries; bootstrap resampling; timeseries analysis

Accepted: 6 March 2022
Published: 8 March 2022
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Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Sensations dynamically change while eating food. Temporal dominance of sensations (TDS) methods [1–3] record the temporal evolution of multiple types of subjectively
reported sensations. These methods have attracted considerable attention in the last
decade [4,5], in part because they are considered more cost-effective than conventional
time-series sensory evaluations such as the time-intensity method [6–8].
Understanding the relationships between primary sensations such as flavor, taste, andtexture, and higher-level sensations or judgments such as freshness and richness of tastes
when eating food products is essential for determining the food values. Relationships
between primary sensations and higher-level human responses, including emotions, havebeen intensively researched using static sensory evaluation methods [9–16]. For example,in [10], primary sensations such as tastes and mouth feels of several vegetables and fruits
were linked with higher-level of attributes such as summery, refreshing, and fresh-made. These

Foods 2022, 11, 781. https://doi.org/10.3390/foods11060781

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2 of 16higher-level attributes are judged based on multi-sensory information, including multiple
primary-sensory attributes. However, few studies have linked primary- and multi-sensory
data over time. Okada et al. [17] developed a method to establish a model of dynamic
causality using temporal data of sensations and emotions related to food intake. Theymodeled the temporal influences among sensory, emotional, and preferential responses
to strawberries using vector auto-regression and Granger causalities. Tachi et al. [18,19]
developed a method for establishing state-space models of temporal dominance responses
for sensations and emotions and used state variables functioning as memories to show howchanges in sensory responses dynamically influence emotional responses. Silva et al. [20]computed the relationship between the temporal changes in liking and the TDS or temporal
dominance of emotions (TDE) results. However, this study did not focus on the relationships between sensations and emotions. Galmarini et al. conducted TDS and TDE tasks
toward coffee during listening musics [21], and investigated how the sensory and emotional
attributes covaried. Their analysis was based on dominance duration and did not aim
for the temporal evolution of sensory and emotional responses. Other dynamic analyses
for TDS methods, such as those performed by [22–24] dealt with only primary sensations
and did not connect their results with time-evolving multi-sensory or complex sensory
responses. Thus, only a few studies have linked the TDS method results for primary- andmulti-sensory attributes. In addition, the studies performed by [17–19] contain results thatcannot be easily interpreted. Thus far, general methods for linking TDS data for primarysensation and higher-level of sensation or feelings have not been established.
This study linked the TDS results for primary- and multi-sensory attributes using
canonical correlation analysis (CCA). This method links two groups of variables using
latent variables, which explains the relationships among multiple variables in the twogroups. One of the biggest differences between this and earlier studies [17,18] that attempt
to link TDS and TDE models is the use of past information in the time-series data. Twoearlier models [17,18] were retrospective and modeled the TDS and TDE results at a set
time point using past information. In contrast, our model is concurrent and computes
the change in an attribute of the TDS task for multi-sensory attributes at a set time point
using changes in the attributes of the TDS task for primary-sensory attributes at the sametime point. Researchers have discussed which retrospective or concurrent model is mostappropriate for analyzing time-series data [25,26]. For example, Chen et al. [25] comparedretrospective, concurrent, and unified analysis of neuroimaging data. However, researchers
have not yet discussed the possibility of using a concurrent model to link TDS tasks. Oneof the purposes of this study was to investigate this possibility.
We also investigated whether raw or differential time-series data (i.e., temporal dominance curves) of the TDS method are appropriate for CCA. Temporal dominance curves
are the main TDS outputs [2] and express the temporal evolution of the proportions inwhich individual attributes are dominant in sensations (see Section 2.1). Raw time-series
data contain long-term trends, but the samples at different moments are not independent,
which may violate the assumptions of CCA or other multivariate analysis techniques. The
effect of long-term trends is removed by differentiating raw time-series data. However,the loss of equilibrium information due to differentiation can be a disadvantage. Thusfar, previous studies have not compared raw and differential time-series data in terms of
TDS data. Hence, we establish two models based on raw (i.e., trend model) or differential
time-series data (i.e., differential model). Then, we discuss their semantic validity.
We used the TDS methods to investigate the temporal evolution of the primaryand multi-sensory responses toward strawberries and statistically linked these responses
by CCA. As aforementioned, thus far, no studies have attempted to link them by using
concurrent models. We subsequently investigated whether the raw or differential timeseries data produce a semantically more reasonable model. Strawberries are popular fruits
used in earlier sensory studies [17,19,27], which can be compared with our results.

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2. Method for Modeling2.1. TDS (Temporal Dominance of Sensations) Method
The TDS method records the evolution of dominant sensory responses during eating.
In this section, we summarize the method. Additional details can be found in [1,4,5]. In the
TDS task, a graphical user interface, as shown in Figure 1, is used. Panels push the start
button as soon as they put food in their mouth. They then push the button corresponding to
the word that best describes the most dominant feelings at each moment. Once the button
is pushed, the button continues to be selected until another button is pushed. There is nolimit to the number of times a button could be selected. The same button can be selected
multiple times, and some buttons may never be selected. When the feelings in the mouth
disappear, the panel presses the stop button to end the evaluation. The time when eachbutton is pressed during the task is recorded. This task is repeated a few times for the same
food by individual participants.

Figure 1. Graphical interface used in a temporal dominance of sensations task. One attribute word isassigned to each button.

Individual trials are normalized to analyze the results of the TDS task when the start
button is pressed until the stop button is pressed. For a single trial, binary functions thatindicate whether the buttons are selected or not at each moment are obtained for eachbutton or attribute. By accumulating the binary functions among all the panels and all trials,
the number of times the attribute is selected is compared to the total number of trials. Thisvalue is called the dominance proportion and is calculated at each moment. Continuous
TDS curves are obtained by smoothing the dominance proportion for each attribute.
2.2. Canonical Correlation Analysis (CCA)
CCA is used to estimate the linear relationship between two sets of variables. Figure 2
shows an overview of the model. We computed the relationships between the primarysensory variables (x) and the multi-sensory variables (y). These variables indicate the valuesof the TDS curves at the moment t. CCA uses latent variables called canonical variables,of which the number is smaller than or equal to the number of variables included in thesmaller set of variables. These values link the variables in the two sets in a reductive manner.
When many variables explain primary and multi-sensory sensations, their relationship canbe easily interpreted by extracting latent variables.
Suppose that there are p primary-sensory attributes and q multi-sensory attributes.
The zero-centered dominance proportions at instance t are denoted by xk (t) (k = 1, · · · , p)
and yl (t) (l = 1, · · · , q). We computed the canonical variables u j and v j aspu j (t) = ∑ a jk xk (t)(1)

v j (t) = ∑ b jl yl (t)

(2)

k =1
q

l =1

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by linear combinations of xk and yk with the coefficients ( a j1 , · · ·, a jp ) and (b j1 , · · ·, b jq ). These
coefficients were determined to maximize the correlation coefficient between u j and v j . For
this calculation, the time functions were discretized into s instances, t ∈ {0, ∆t, ..., 1 − ∆t, 1}.
Here, t = 0 and t = 1 denote the start and end of the normalized task duration, respectively.
Further, ∆t is the sampling interval: ∆t = 1/(s − 1).

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Multi-sensation 2: ‫ݕ‬ଶMulti-sensation ‫ݍ‬: ‫ݕ‬௤Dominance proportion

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Canonical variable

TDS curves for
multi-sensory
responses

‫ݕ‬ଵ
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0

Normalized time

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Figure 2. Schematic image of canonical correlation analysis (CCA). CCA connects p primary-sensory
variables (x) and q multi-sensory variables (y) using the canonical variables u and v. The coefficientsa j and b j were determined to maximize the correlation coefficient between u j and v j . j = 1, · · ·, r(r ≤ min( p, q)).

The correlation coefficient between u j and v j is greater than that between u j0 and v j0
when j < j0 . Furthermore, u j and u j0 (j 6= j0 ) are independent of each other. This is also true
for v j and v j0 . In CCA, the number of canonical variables can be arbitrarily determined,with the maximum being min( p, q). This study determined the number of variables using
Pillai-Bartlett’s test.
3. Attribute Selection and Categorization
3.1. Task
First, we used a questionnaire to select the attributes used in the two types of TDS
tasks. Panels ate strawberries and, in a check-all-that-apply manner, selected adjectives
that expressed what they felt while eating. The adjective list was prepared by referring
to [27,28] and other literature, including websites and food magazines. The list contained
218 adjectives that describe sensations and eating experiences. The adjectives were randomly arranged on a paper and provided to the panels. Then, they categorized the selected
attributes into three categories: primary-sensory, multi-sensory, and evaluative. The definition of each category was as follows. Primary-sensory attributes were gustatory, osmatic,
and textural aspects. Multi-sensory attributes were related to higher-level sensations that
occur while tasting or were based on multiple sensory aspects. Evaluative attributes described the overall significance and importance of the tasting experience and the food value.
A similar categorization task was conducted in earlier sensory evaluation tasks (e.g., [29]).
The task was conducted in a quiet office room at approximately 28 ◦ C. The panel ate
three strawberries in total before and during the task.
3.2. Panels
The panels were 10 university students (8 male and 2 female participants older than
20 years). None of the participants studied or worked in the food industry. They took partin the experiments after providing written informed consent.
3.3. Food Specimens: Strawberries
Tochiotome strawberries (the cost was approximately 750 JPY for seven strawberries)
were used for the experiments. The strawberries were purchased at a fruit market on the
day of the experiment. The whole strawberry could be put in the mouth.

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3.4. Results
Table 1 lists the attributes that gained more than seven votes. The left column inTable 1 lists the attributes that all participants fully agreed upon, with their categories.
These attributes include 14 primary-sensory attributes, six multi-sensory attributes, andfive evaluative attributes. The primary-sensory attributes were mostly of unisensory cues
related to either of taste, osmatic, and texture. One exception may be green, which participants agreed to categorize as primary-sensory attributes. Green or unripeness is largely
judged based on the aroma and taste of unripe and green fruits [27,30]. Although green
is of multi-sensory cues, the non-food expert participants agreed it as a primary attributeof strawberries. The right column of Table 1 lists seven attributes that spanned multiple
categories. Four attributes spanned two categories, and three attributes spanned all thecategories. Some attributes such as earthy and caramel used by experts of strawberries [27]did not gain substantial votes.
Table 1. Results of the attribute selection task. The attributes gained more than 7 of the 10 votes.
(upper part) Attributes that the panels fully agreed upon and their categories. (lower part) The
panel’ opinions on categorization disagreed. The values in parentheses are the number of votes foreach attribute.

Primary-Sensory

Multi-Sensory

Evaluative

sweet
sour
hard
soft
waterycrispy
juicy
moist
green
fruityberry
weakstrongsmoothrefreshingpleasurable
unripefresh
lovingdelicious
satisfiedgood
luxuryPrimary-Sensory

Multi-Sensory

Evaluative

aromatic (3)rich/deep (4)aromatic (4)rich/deep (4)wonderful (3)elegant (3)light (3)ripe (3)mild (3)wonderful (5)elegant (4)light (2)ripe (4)mild (2)light (4)ripe (3)mild (2)To select the attributes for the two types of TDS tasks, we determined the categories of
the attributes listed in Table 1 (upper part) by considering the number of votes. In terms ofaromatic, rich/deep, light, and ripe, we decided their categories based on panel discussion
and interviews with the participants. Aromatic was categorized as a primary-sensory word
because it largely depends on osmatic aspects for strawberries. Rich/deep, both of whichwere semantically similar and were attributed to multiple intensive tastes of strawberries,
were categorized as multi-sensory attributes. Light was categorized as primary-sensory
attributes because several participants mentioned that light was related to the duration ofsweet taste or weakness of tastes, and sweet and weak were also categorized as primarysensory words. Ripe was categorized as multi-sensory words since ripeness is judged basedon multiple sensory attributes comprising taste and smell. Finally, in terms of the attributes

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in Table 1, aromatic and light were categorized as primary-sensory attributes, rich/deep, ripe,and mild were categorized as multi-sensory attributes, and wonderful and elegant werecategorized as evaluative words.
4. Experiment: TDS Tasks for Primary- and Multi-Sensory Attributes
4.1. Words Used in the Tasks
Informally, eight domestic members in the authors’ institute conducted preliminary
TDS tasks using the set of primary- and multi-sensory attributes described in Section 3. In asingle task, only one attribute set was used, in line with ISO 13299 [2]. The attributes whose
dominance proportions did not reach significance were removed. The significance level
was calculated by using the binomial distribution [2]. As a result, eight primary-sensory
attributes and six multi-sensory attributes were selected for the main experiments. Table 2
lists the attributes used in the main TDS tasks and their descriptions. We did not use
evaluative attributes because there are few earlier studies on temporal dominance tasks forevaluative attributes and the protocol has yet to be established.
Table 2. Attributes used in the TDS tasks.
Primary-Sensory Attributes

Description

Sweet
Sour
FruityGreen
WateryJuicy
Aromatic
Light

Basic taste. No definition was provided.
Basic taste. No definition was provided.
Smell of sweet fruits.
Smell, taste, and mouth feel of grass or unripe fruits.
Water content with no strong taste.
Amount of juice and flesh.
Complex but pleasant smell.
Sweet taste that does not last long in the mouth.

Multi-sensory attributes
Refreshing
Fresh
Pleasurable
Rich/deep
Ripe
Mild

Pleasantly cool.
Recently harvested.
Feeling of pleasure.
Combination of multiple strong tastes or aromas.
Fully grown and ready to be eaten.
Taste spreads gently without strong stimuli.

4.2. Tasks
Before conducting the main tasks, the panels agreed with the descriptions of attributesin Table 2 and used graphical user interfaces to familiarize themselves with the TDS tasks.
The attribute buttons were randomly configured on the interfaces for individual panels.
The graphical user interface such as the one shown in Figure 1 and data collection software
were made by the authors and run on Matlab (ver. 2019a, Mathworks Inc., Natic, MA, USA).
In the main tasks, the participants rinsed their mouths before each trial. They selecteda strawberry with their non-dominant hand, keeping their eyes closed and pinching theirnose by the dominant hand. Some properties of strawberries including freshness are
influenced by their appearances [31]; hence, we shut out the visual cues. Further, theirodors could influence the task even before eating; hence, we also shut out the olfactory
cues. After they put the whole strawberry into their mouth, they immediately started
the evaluation using the graphical user interface with their eyes opened and their handreleased from their nose. The panels then selected the button corresponding to the mostdominant feeling at each moment, as mentioned in Section 2.1. There was no time limit for
the task. Each task lasted approximately 30 s. Each panel performed three trials for eachcategory of sensory attributes. Nine participants performed the task using primary-sensory
attributes first, while eight participants performed the task using multi-sensory attributes
first. The experiments were conducted at least 1 h after a meal.

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7 of 164.3. Panels
The panels included 17 university students (14 males and 3 females, age: 20–25) from
Nagoya University. They were called for by using posters in the university buildings. Noneof them studied or worked in the food industry and had declared sensory disabilities. They
took part in the experiments after providing written informed consent.
4.4. Results(b)

Dominance proportion (-)(a)

Dominance proportion (-)

Figure 3 shows the dominance proportion curves obtained from the tasks. The horizontal axis is the time normalized by the time from the moment the start button was pressed tothe time the stop button was pressed (average: ∼30 s). The curves were smoothed using amoving average filter with a time window of 1/30 normalized time.
0.50

sweet
sour
fruitygreen

0.25

wateryjuicy
aromatic000.5
Normalized time (-)

1

light
significance level
chance level

0.50

refreshing
fresh

0.25

pleasurable
deep
ripe

0
0

0.5
Normalized time (-)

1

mild

Figure 3. Dominance proportion curves for strawberries. (a) TDS curves of primary-sensations.
(b) TDE curves of multi-sensations.

Regarding primary-sensory attributes, sweet, juicy, and watery were dominant at the
beginning of the evaluation (0–0.2 normalized time). Attributes related to fragrance, such as
fruity and aromatic, became dominant at 0.2–0.3 normalized time. Sourness was prominent
from the middle to the second half of each task. Similar curves were observed in the resultsof the TDS method for strawberries [17]. Regarding the multi-sensory attributes, fresh was
dominant at the beginning. Then, ripe and deep became prominent. Refreshing and mild
were mostly dominant after approximately 0.3 normalized time. In particular, refreshing
was prominent at the end of the task.
5. Model Linking Primary- and Multi-Sensory Attributes
5.1. Data Analysis
We discretized the normalized time into 30 instances, as described by [17]. In our study
and [17], a single evaluation task took approximately 30 s on average. It took a minimum of
approximately 1 s from when the participant selected a certain button to when they selected
the next button. We also obtained the difference between the dominance proportions attime t and t − ∆t as the differential value at t. The first two discretized points were not usedin the model computation. This is because almost no descriptors were selected immediately

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after the start button was pressed in the TDS methods. As mentioned in Section 2.2, wecomputed CCA for the dominance proportions of sensory attributes using R 4.0.3 (cca 1.2.1,
ccp 1.1).
5.2. Bootstrap Resampling
Using a general data analysis method for TDS methods [2], a single set of dominanceproportion curves is obtained from all the panels. As mentioned above, the curves are
discretized into 30 instants to compute the CCA. We obtained 28 samples from a single
set of curves, excluding the first two discretized instants. However, CCA requires more
samples to avoid overfitting. Hence, we increased the number of samples by bootstrap
resampling, as described in [32–34]. We resampled 51 samples with replacements fromthe original sample set (17 participants × three repetitions, 51 samples total) obtained in
the TDS tasks to calculate a set of TDS curves. We then repeated this process ten times.
Subsequently, we obtained ten sets of TDS curves for each primary- and multi-sensory
attribute, including 300 and 290 discretized instants for the trend and differential models,
respectively. As mentioned above, we did not use the first two instants. Hence, the numberof discretized instants used in the analysis were 280 and 270 in the trend and differential
models, respectively.
5.3. Number of Canonical Variables
Table 3 lists the contribution proportions of the canonical variables for the trend
and differential models. According to Pillai-Bartlett tests with a significance level of
0.05, five and four canonical variables were adopted for the trend and differential models,
respectively. For the trend model (Table 3 (upper part)), the five canonical variables covered
73% of the variance of the dominance proportions of primary-sensory attributes and 87%of the variance of the multi-sensory attributes. For the differential model (Table 3 (lowerpart)), four canonical variables captured 50% and 58% of the variance of the differential
dominance proportions of primary- and multi-sensory attributes, respectively.
Table 3. Contribution of canonical variables to the sample variances in the (top) trend modeland (bottom) differential model. The canonical variables are arranged in the order of correlation
coefficients between the dominance proportions of primary- and multi-sensory attributes.

Trend Model
Canonical
Variable

Contribution toPrimary-Sensations

Contribution toMulti-Sensations

Pillai-Bartlett
Trace

F-Valuep-Value

1st
2nd
3rd
4th
5th
6th

0.14
0.33
0.10
0.07
0.09
0.09

0.20
0.31
0.16
0.08
0.12
0.13

2.06
1.12
0.42
0.21
0.08
0.01

17.7
10.7
5.20
3.93
2.69
1.17

0.000
3.2 × 10−14
3.3 × 10−14
5.2 × 10−7
0.006
0.319

Canonical
Variable

Contribution toPrimary-Sensations

Contribution toMulti-Sensations

Pillai-Bartlett
Trace

F-Valuep-Value

1st
2nd
3rd
4th
5th
6th

0.05
0.17
0.14
0.14
0.13
0.13

0.10
0.16
0.15
0.17
0.21
0.20

1.38
0.48
0.18
0.09
0.02
0.01

10.1
4.03
2.08
1.71
0.85
0.47

0.000
4.0 × 10−14
0.002
0.044
0.556
0.703

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5.4. Canonical Correlation Models and Interpretation of Canonical Variables
5.4.1. Models Computed from Trend TDS Curves
Figure 4a,b show the time series of the canonical values of the trend model. As shownin Table 4, the first canonical values of the primary- and multi-sensory attributes were
nearly equally affected by all the attributes. The first canonical variables determined theaverage size of the TDS curves for all attributes. As shown in Figure 4a,b, the first canonicalvalues were small at the beginning of the task and were almost constant after 0.2 normalized
time. As mentioned before, buttons may not be selected immediately after the task starts
in the TDS tasks. Thus, the initial values of the dominance proportion curves are small.
The first canonical variables show whether a time point is at the beginning of the task ornot. It is not important to understand the connection between primary and multi-sensory
attributes. A similar latent factor was reported in the principal component analysis of TDS
curves [35].
(b) Canonical values for multi-sensory words (trendmodel)

22

0

0

Score (-)

Score (-)(a) Canonical values for primary-sensory words (trendmodel)

2
1st canonical value
2nd canonical value
3rd canonical value

4
0

2
1st canonical value
2nd canonical value
3rd canonical value

4th canonical value
5th canonical value

4

0.5
Normalized Time (-)01.0(c) Canonical values for primary-sensory words (differential
model)

4th canonical value
5th canonical value

0.5
Normalized Time (-)1.0(d) Canonical values for multi-sensory words (differential
model)

20

Score (-)

Score (-)

2

2
1st canonical value
2nd canonical value

40

3rd canonical value
4th canonical value

0.5
Normalized Time (-)

1.0

0
2
1st canonical value
2nd canonical value

40

3rd canonical value
4th canonical value

0.5
Normalized Time (-)

1.0

Figure 4. Temporal changes of canonical values corresponding to the primary-sensory (a,c) andmulti-sensory attribute words (b,d). (a,b): Canonical values of the trend model. (c,d): Canonicalvalues of the differential model.

Regarding the second canonical value, juicy and fruity had a greater influence (Table 4).
These values can be interpreted as representing the fruitiness of taste and smell. In themulti-sensory attributes, ripe was prominent. The correlation coefficient of the second
canonical variables between the primary- and multi-sensory attributes was as large as 0.83
(Table 4). According to Table 3, the contributions of the second canonical variables were thelargest among all the canonical variables for the primary- and multi-sensory categories. The
values of these canonical variables were large in the first half of the task, peaked around0.25 normalized time, and then gradually decreased. A study using principal component
analysis of static sensory evaluation of strawberries [27] reported that the fruity aroma andFoods 2022, 11, 781

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juiciness of strawberries are the most prominent features for classifying ripe strawberries.
The features in [27] correspond to the second canonical variables in the present study.
Table 4. Coefficients for 1st–5th canonical variables when the raw (i.e., trend) TDS curves wereadopted for computing CCA. Correlation coefficients were computed between the two canonicalvariables for the primary- and multi-sensory attributes for each of the five canonical variables.
Canonical Variables
Primary-sensory

1st

2nd

3rd

4th

5th

sweet
sour
fruitygreen
wateryjuicy
aromatic
light

17.4
17.1
17.6
17.5
15.7
16.4
17.0
17.8

5.5
2.6
10.4
1.0
5.8
9.6
4.0
4.7

5.0
3.8
4.8
11.3
26.4
5.9
8.6
11.0

0.1
12.0
−2.8
2.9
9.8
14.2
6.8
5.1

15.0
3.5
3.0
18.3
2.9
15.4
6.3
3.3

Correlation

0.97

0.83

0.47

0.36

0.25

Multi-sensory

1st

2nd

3rd

4th

5th

refreshingfreshpleasurable
rich/deep
ripe
mild

11.8
11.5
11.8
12.0
11.4
12.0

0.8
7.2
6.2
5.2
13.4
3.2

9.9
4.5
1.4
4.1
6.9
4.4

12.2
21.3
16.9
9.5
−2.5
5.4

4.7
13.1
−5.1
−1.5
4.9
11.2

For the third canonical variable, as in Table 4, watery, has the highest coefficients inthe sensory attributes, followed by light and green. Refreshing is representative of the multisensory attributes. Hence, this canonical value can be interpreted as a cool feeling (light,
green, and refreshing) with high water content. The correlation between the third canonicalvariables for the primary- and multi-sensory attributes was 0.47 (Table 4). The contribution
to the primary-sensory attributes was 0.10, and that to the multi-sensory attributes was 0.16
(Table 3). The third canonical values are high at 0.1–0.2 normalized time, indicating that thewater content in the strawberries was felt after the first bite (Figure 4a). The values werealso high near and after 0.6–0.7 normalized time, when refreshing is prominent (Figure 4b).
Regarding the fourth canonical variables, juicy, sour, and watery exhibited large coefficients among the sensory attributes. These results are consistent with those of Oliver et al.,
where juiciness and sourness constituted a major component of the static sensory analysis
for strawberries [30], although they did not use watery attributes in their study. Freshexhibited a large coefficient among the multi-sensory attributes, followed by pleasurable andrefreshing. The correlation between the canonical variables was moderate (0.36), and thecontribution rates were 0.07 and 0.08 for the sensory and multi-sensory attributes, respectively, which were the smallest among all the canonical variables. These results suggest thatthe fourth canonical variables may not be important in explaining the behavior of the entire
dominance proportion curves. The fourth canonical variables are interpreted as features ofstrawberries, with a lot of pulp, juice, and sourness, leading to perceived freshness.
For the fifth canonical variables, green, juicy, and sweet were prominent among the
primary-sensory attributes. Among the multi-sensory attributes, fresh and mild were prominent. The fifth canonical variables exhibited peaks at the beginning and middle of the task,
as shown in Figure 4a,b. These canonical values are difficult to interpret. According to[27,30], sweet and green flavors of strawberries are perceptually opposite, which does notmatch the coefficients of the fifth canonical variables. The correlation coefficient of the fifth
canonical variables between the primary- and multi-sensory attributes was low (0.25), andthe contribution rates were 0.09 and 0.12 for the primary- and multi-sensory attributes,

Foods 2022, 11, 781

11 of 16

respectively. Therefore, the fifth canonical variable may not be important for interpreting
the relationships among the TDS curves of dominance proportions of strawberries.
5.4.2. Models Computed from Differential TDS Curves
Figure 4c,d show the time series of canonical variables when the differential curvesof dominance proportions were used for CCA. The contribution ratios of all the canonicalvariables were similar, ranging from 0.10 to 0.21 (Table 3). The first–fourth canonicalvariables explained 50% and 58% of the differential curves of temporal dominance forprimary- and multi-sensory attributes, respectively. For the multi-sensory attributes, thecontribution ratios of the fifth and sixth canonical variables that were rejected by the
Pillai-Bartlett test were relatively large, suggesting that the model could not effectively
summarize the differential curves of the dominance proportions.
As listed in Table 5, the first canonical variables were equally affected by all attributes
in the primary- and multi-sensory categories. These variables indicate the trends common
to all the attributes. As shown in Figure 4c,d, the canonical variables were small at the
beginning of the task and were generally constant after 0.2 normalized time. Similar to thefirst canonical variables for the trend model in Section 5.4.1, the first canonical variables forthe differential model indicate the elapsed time and do not contribute to understanding therelationship between the primary- and multi-sensory attributes.
Table 5. Coefficients for 1st–4th canonical variables when the differential values of the dominanceproportion curves were used for CCA.

Primary-Sensory

1stCanonical Variables
2nd
3rd

4th

sweet
sour
fruitygreen
wateryjuicy
aromatic
light

18.9
16.1
18.5
17.0
16.9
18.1
18.1
16.6

−7.2
3.2
12.0
−0.87
−9.3
−2.5
1.1
17.6

−6.5
9.7
−13.4
−5.9
−7.4
5.9
13.4
6.6

1.5
1.9
8.0
−29.9
−3.6
−5.5
2.2
−15.2

Correlation

0.95

0.55

0.29

0.26

Multi-sensory

1st

2nd

3rd

4th

refreshingfreshpleasurable
rich/deep
ripe
mild

14.0
15.0
13.2
11.2
14.1
13.2

−7.5
−3.9
2.2
21.0
7.1
8.4

10.0
7.2
4.1
12.9
−21.0
4.5

−3.2
−5.2
26.6
−0.79
−0.83
−7.4

As shown in Table 5, the second canonical variables are positively affected by lightand fruity and negatively affected by sweet and watery. Rich/deep influenced the canonicalvariable among the multi-sensory attributes. Overall, these results are difficult to interpret.
It seems semantically reasonable that rich/deep, which indicates strong taste, increased as
fruity increased and watery decreased. However, it seems paradoxical that strawberries
feel richer and deeper when lighter and less sweet. These canonical values exhibited apeak around 0.2 normalized time and continued to have a value close to 0 after that. The
correlation coefficient between the two canonical variables was 0.55, and the contribution
ratios were 0.17 and 0.16 for the primary- and multi-sensory attributes, respectively.
For the third canonical variables describing the primary-sensory attributes, fruity
negatively affected the canonical variable, and aromatic and sour positively affected thevariable. Among the multi-sensory words, the negative influence of ripe was the mostprominent, followed by positive influences by rich/deep and refreshing. Some of theseFoods 2022, 11, 781

12 of 16connections were easy to interpret, whereas others were difficult to interpret. In a sensory
study of strawberries [27], strawberries with a stronger fruity aroma and less sourness were
judged to be riper, which is partly in line with the third canonical variable. In contrast, fruity(defined as the aroma of sweet fruits) and aromatic (pleasant smell) had opposite directions.
Further, ripe and rich/deep also had opposite directions. The opposite directions for fruity,
aromatic, ripe, and rich/deep appear contradictory, which makes the interpretation of thethird canonical variables less intuitive. The correlation coefficient of the third canonicalvariables between the primary- and multi-sensory attributes was as small as 0.29.
Regarding the fourth canonical variables, green and light exhibited a strong influenceon the canonical variable among the primary-sensory attributes. Furthermore, pleasurable
was the most influential multi-sensory attribute. As shown in Figure 4c,d, these canonicalvariables were roughly neutral or positive at the beginning and end of the task and negativeat the middle of the task. However, the values fluctuated rapidly, and no clear trends wereobserved. The correlation coefficient between the canonical values for sensory and multisensory attributes was 0.26. The meaning of the fourth canonical variables can be easilyinterpreted. Green-smelling and light-tasting strawberries lead to negative (unpleasurable)
feelings.
6. Discussion
Our study results show that trend or differential models are suitable for computing
the CCA of the concurrent values in dominance proportion curves. In the trend model, fivepairs of canonical variables connected primary- and multi-sensory attributes. According toTable 3, 73% and 87% of the variances of the dominance proportion values of the primaryand multi-sensory attributes, respectively, were explained by these canonical variables.
These values may be compared with those of trajectory plots of TDS curves in earlier
studies. For example, Nguyen et al. [35], Lenfant et al. [36], Merlo et al. [37], and Nguyen
and Wismer [38] reported that principal components explained 52–83% of the variancesof temporal dominance curves while eating wheat flakes, yoghurts, and hamburgers,
respectively. Although there is no standard about these values, those in the present study
and earlier studies are comparable, indicating that the TDS curves were well explained by
the latent variables. The number of latent variables was smaller than the number of multisensory attributes (six) by only one variable. Thus, the contraction effect of the model was
not large. The meaning of four of the five canonical variables can be reasonably interpreted,but it is difficult to interpret the fifth canonical variables. In the differential model, primaryand multi-sensory attributes were connected by four pairs of canonical variables. According
to Table 3, 50% and 59% of the variances in the differential dominance proportions ofprimary- and multi-sensory attributes, respectively, were explained. Interpreting the second
and third canonical variables was difficult. Considering these points comprehensively,
the trend model is more suitable for analyzing dominance proportion curves than the
differential model. However, we cannot conclude that either model is superior solely fromthe examples of strawberries. Similar studies are necessary for other foods in the future.
Figure 5 summarizes the trend model for strawberries with major connection lines
between the attributes and canonical variables. Note that the first canonical variable pair
indicates the time elapsed and is not shown. The canonical variables were named forconvenience. These four types of canonical variable pairs determine the dynamic behaviorof the dominance proportions of strawberries. In the early phase of the eating experience, juicy-fruity (second canonical variable) and juicy-sour (fourth canonical variable)
factors are prominent primary-sensory aspects. These factors are likely caused by thejuice coming out after initial biting and indicate that the fruity aroma and taste associatedwith the juice caught the attention of the panels. The participants also felt the ripenessand freshness of strawberries during the early phase. This agrees with [31] where thefreshness of strawberries was largely judged by juiciness after their visual factors suchas the surface shine and bruises. In our model, ripeness was not related to green whereas
unripe strawberries were characterized by the green taste and aroma in a study usingFoods 2022, 11, 781

13 of 16

strawberries [27]. This difference may be because underripe strawberries were intentionally
included in [27] whereas apparently underripe strawberries were not used in the present
study. The third and fifth canonical variables were relatively large in the middle phase. As
shown in Figure 3a, no sensory attributes were dominant in this phase, leading to a feelingof mildness (represented by the fifth canonical variable pair). Subsequently, a refreshingfeeling becomes dominant (as represented by the third canonical variable). The last phase isrepresented by the fourth canonical variable pair, which represents an intensely sour taste
and refreshing feeling. Refreshing was defined as “pleasantly cool” as in Table 2. In general,
this attribute is favorable in evaluating fruits [39]. In [39], refreshing was felt for juicy, sweet,
and sour mandarins, and refreshing appeared to be judged based on multiple types of basic
tastes. For the strawberries in the present study, refreshing was connected with sour andjuicy by the fourth canonical variable. Furthermore, refreshing was prominently connectedwith watery by the third canonical variable. The meanings of refreshing may differ among
different types of fruits; however, regarding strawberries and mandarins, sourness is apotentially common factor. For strawberries, sourness in the last eating phase is generally
accepted by consumers [40].
Multi-sensory attributes
Refreshing

Pleasurable

Fresh

Mild

Ripe
13.4

12.2

13.1
Freshrefreshing(v4)

Refreshing(v3)

Cool (u3)26.4

Mild-fresh(v5)

Ripe (v2)

Early andlast phases

Middle phase

11.0

11.2

21.3

16.9
9.9

Early phase

Juicy-sour(u4)Juicy-fruity
(u2)

12.0

9.6

14.2

Middle phase
u5
18.3

10.4
15.4

15.0

11.3
Light

Green

Watery

Sour

Juicy

FruitySweet

Primary-sensory attributes

Figure 5. Trend model for strawberries. Four pairs of canonical variables are shown, except for the firstcanonical variables. The canonical variables were named by considering the prominent coefficients of
the attributes in Table 4. The fifth canonical variable (u5 ) for the primary-sensory attributes is notnamed. The major connections are shown. Values nearby the lines indicate their coefficients.

Our study suggests that the concurrent model can link the results of the TDS methods
for primary- and multi-sensory attributes. However, in order to determine which of the
concurrent or retrospective models are more suitable, a variety type of foods need to betested whereas only strawberries were tested in the present study. The two models, i.e., theconcurrent model in this study and retrospective models in [17,18] contain some points
that remain semantically validated. Both models need further studies.
We conducted two types of TDS tasks by using primary- and multi-sensory attributes.
According to ISO standards [2], very different attributes, such as sensory and emotional
attributes, should not be tested in the same temporal dominance tasks. However, there isno general method to classify very different sensory attributes. Hence, following earlier
studies [29], we classified attributes into three categories as in Section 3. We found that
attribute categorization is difficult because some attributes span multiple categories. A similar problem was reported in [29] where several attributes among more than one hundred
for describing experiences related to touch were categorized as both sensory and evaluative. The panels in the present study categorized pleasurable, which is the translation of
kokochiyoi in Japanese, into multi-sensory; however, pleasurable also may include the aspectFoods 2022, 11, 781

14 of 16of evaluative attributes. Kokochiyoi can be also translated as comfortable and delightful.
The results of attribute categorization task may depend on the cultures and expertise of thepanels considering that some attributes used by the experts, such as earthy and caramel [27]were not selected by the non-expert panels in the present study. Different categorization
leads to different results of CCA, which limits the generality of our approach. Furthermore,
the present study lacks a demonstration of uncertainties of canonical variables. As shownin Figure 4, some canonical values fluctuate around zero at some moments. It is meaningful
to know whether those values are statistically different from zero.
7. Conclusions
Despite the growing usage of TDS methods in food industries, no previous studies
have proposed to link the TDS curves for the sensory attributes in different categories.
We established a model to link these curves using the concurrent dominance proportions
and CCA values. We investigated whether the trend or differential models are better interms of the semantic validity of the canonical variables and the dominance proportions
explained by the canonical variables. We then determined that the trend model is better for
strawberries, in which five pairs of canonical variables represent the temporal evolution of
the dominance proportions. The proposed method will increase the comprehension of the
dynamic relationships between primary- and multi-sensory attributes while eating foods
based on fewer latent factors, i.e., canonical variables. The method is expected to be usefulespecially when the number of attributes in the two categories is large. We also found thatcategorizing primary- and multi-sensory attributes are not easy, and even the trend model
includes a semantically unclear pair of canonical variables.
Author Contributions: Conceptualization, N.S. and S.O.; methodology, N.S. and S.O.; software,
N.S.; validation, N.S. and S.O.; analysis, N.S. and S.O.; data curation, N.S.; Writing —original draft
preparation, N.S. and S.O.; writing—review and editing, N.S., S.O., A.Y. and Y.Y.; Supervision, S.O.,
Y.A. and Y.Y.; project administration, S.O.; funding acquisition, S.O. All authors have read and agreed
to the final version of the manuscript.
Funding: The study was in part supported by the Tojuro Iijima Foundation for Food Science andTechnology (2020-40).
Institutional Review Board Statement: The study was approved by the Institutional Review Boardof the School of Engineering, Nagoya University (#20-20).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data used in the present study are available at TDbear project athttps://www.comp.sd.tmu.ac.jp/hci/tdbear/ (accessed on 5 March 2022).
Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations
The following abbreviations are used in this manuscript:
TDS
CCA

Temporal dominance of sensations
Canonical correlation analysis

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