JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 20191

Affective Dynamics: Principal Motion Analysis of
Temporal Dominance of Sensations andEmotions Data
Shogo Okamoto, Member, IEEE, Yuki Ehara, Takumu Okada, and Yoji Yamada, Member, IEEE
Abstract—Temporal dominance (TD) methods can be used to record temporal changes in multiple sensory and affective responses.
TD methods have found wide applications in the analysis of eating experiences of humans. However, extant analyses performed on TD
data do not fully utilize the time-series properties of such data. The present study validates the prospect of principal motion analysis(PMA) of TD data. PMA is an extension of principal component analysis, and can be used to resolve multivariate motion data into baseprincipal motions. In this study, panelists were asked to evaluate the tastes of ten types of pickled plums using the temporal dominanceof sensations (TDS) and emotions (TDE) methods. Additionally, the panelists were asked to rate the plums using the semantic
differential method. Results obtained using both methods were observed to demonstrate good agreement with each other in terms of
the structures of reduced variable spaces. As realized in this study, implementation of the combined TD–PMA approach can potentiallyfacilitate statistical discrimination of all food products, whereas conventional methods, such as principal component analysis of data
provided via use of the semantic differential method, can at best discriminate only 67% of product pairs. PMA can, therefore, be
considered as a suitable technique to reveal the characteristics of TD data.
Index Terms—Principal motion analysis, Temporal dominance, Semantic differential method

F

1

I NTRODUCTION

There exist many evaluation techniques that facilitate
investigation of the sensory and affective aspects of eating
experiences. Some of the most common and successfully
employed approaches in this regard are scoring methods that employ descriptive norms. These include thesemantic differential (SD) method, quantitative descriptive analysis, and sensory proﬁling. During applicationof these methods, panelists evaluate foods after tastingthem or after a period of contemplation. In contrast, thetrace [1], time-intensity [2], and temporal dominance ofsensations/emotions (TDS/TDE) methods [3], [4], [5] can be
employed to capture temporal changes in the gustatory, osmatic, textural, and affective experiences that occur during
the time interval between the commencement and end of
tasting. In this paper, the acronym “TD” refers to both TDS
and TDE. Recently, the TD method has attracted increased
attention on account of its inherent advantages with regard
to effective collection of multiple response types, whereas
the trace and time-intensity methods can only be used torecord a single, or at best two, response types using descriptive norms [6], [7]. Thus far, the TD method has been used
for evaluating such food products as cheese [3], [8], wine [9],[10], chocolate [4], strawberries [11], fruit juices [12], andpickles [13].
As described in section 2, TD methods record the temporal evolution of dominantly experienced sensory and••S. Okamoto, T. Okada, Y. Ehara, and Y. Yamada are with the Departmentof Mechanical Systems, Nagoya University, Japan.
E-mail: shogo.okamoto@mae.nagoya-u.ac.jp
This study was approved by the institutional review board, School ofEngineering, Nagoya University (#15-12).

Manuscript received April 1, 2019; revised xx xx, 2019.

affective statuses of individuals during eating. That said, TD
methods are still relatively new, and a generalized method
for analysis of temporal changes in measured data is yet
to be established. Extant studies concerning the TD method
have mainly focused on the investigation of properties ofdata obtained using the TD approach [3], [14], [15], comparison of the TD approach against other sensory evaluation
methods [3], [9], [12], [16], [17], [18], pursuit of experimental protocols [12], [19], [20], [21], [22], and availability forvarious types of edible products. To facilitate analyses of
TD data, extant studies [9], [13], [15], [18], [23], [24] have
employed techniques involving visual inspection of time
proﬁles or statistical analyses of characteristic values computed from them. A typical example of one such time-proﬁlecharacteristic value is referred to as the dominance duration
during which a certain sensory or affective attribute isreported as being dominant [9], [13], [24], [25], [26]. Other
examples include the maximum value of each attribute typewithin the time proﬁle. These characteristic values do not
capture the dynamic or time-evolutionary aspects of TD
data; to address this issue, Lepage et al. [20] split the entire
eating duration into three phases, and subsequently computed characteristic values for each phase. Further, Franczak
et al. [27] and Lecuelle et al. [28] modeled temporal andstochastic transitions concerning the most salient eating
experiences using the Markov model. Nonetheless, theseapproaches do not fully utilize the temporal evolution of
TD data.
This study aims at investigating the ability of the principal motion analysis (PMA) technique [29], [30] to resolve
multivariate-motion TD data into base principal motions.
As described in section 3, PMA has primarily been used
for analysis of human motions, and has thus far not been

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019

applied to TDS data. Although PMA does not yield a uniquesolution, it is considered suitable for use in this study owing
to its ability to analyze multivariate time-series data using
only a small number of base functions called principal
motions. PMA is, therefore, generally compatible with moststatistical testing methods (such as analysis of variance)
while maintaining time-series proﬁles of sensory and affective attributes. To collect test data, TD tasks were performedin combination with static sensory evaluation tasks (e.g.,
the semantic differential (SD) method) for ten types ofplum pickles. Subsequently, results obtained using the two
approaches were compared to statistically discriminate thefood products. Furthermore, the semantic validity of results
obtained using PMA was compared against that of results
obtained using the SD approach. Part of the experimental
data recorded and subsequent analyses performed in thisstudy were presented in [13] during a comparison of results
obtained via principal component analysis of SD and TDS
data.

2

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This section provides an introduction to the temporal dominance method in order to facilitate easy understanding of
the remainder of this paper. For further details, readers arereferred to [31], [32]. As already stated, the TD method
effectively records evolutionary changes in multiple typesof subjective experiences that occur during eating, drinking,and smelling [3]. Sensory evaluation methods, such as the
SD method and sensory proﬁling, statically capture multiple types of verbally described attributes. Trace and timeintensity methods capture dynamic changes within single
attributes. Intuitively, it can be realized that TD methods
somewhat correspond to a combination of these two conventional approaches. Thus far, TD methods have mostly
been used for evaluation of eating experiences; however,they can potentially be used for evaluation of other nonvisual attentive experiences as well.
TD tasks employ graphical user interfaces (GUI) on acomputer screen, as depicted in Fig. 1 (a), and instants at
which panelists make a selection by tapping on the screen
are recorded. Options visible on the screen are marked
by words describing the gustatory, osmatic, textural, or
affective experiences. In the case of eating tasks, panelists
select the “start” option immediately after placing the foodin their mouths, at which instant the task is initiated. At
arbitrary time instants, panelists are asked to select an
option with a description that best corresponds to theireating experience at that instant. As depicted in Fig. 1 (b),panelists subsequently make other selections corresponding
to changes in their experience. The system is designed suchthat once a selection is made, it would automatically apply
to all subsequent experiences until another selection has
been made. At each instant, only one selection can be made.
The same selection can be made multiple times during
a single operation, whereas certain options may remain
unselected throughout. This operation continues until panelists swallow the food specimen, and the “stop” option is
selected. With regard to option selection criteria, instructions
provided to panelists may inﬂuence their selection behavior [14]. In our experiments, in accordance with the studiesŶĚĞĂƚŝŶŐ

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Fig. 1. Temporal dominance (TD) method–(a) Graphical user interface
for option selection; (b) Data obtained during single operation; (c) Aggregation of responses recorded from all panelists for each descriptor; (d)TD curves obtained upon completion of smoothing operation. The curve
indicates temporal changes in frequencies at which each response wasrecorded at each instant.

performed by Pineau et al. [3] and other researchers [12],[24], the panelists were instructed to make selections basedon those experiences that drew their attention the most.
For each above-described operation, binary functions oftime are acquired to demonstrate whether corresponding
attributes have or have not been selected at each instant.
These binary functions are normalized with respect to thetime instant at which the “stop” option is selected by

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019

panelists. Binary functions obtained for each attribute1 areaggregated among all operations, as depicted in Fig. 1 (c).
These aggregated values are then divided by the number ofoperations to compute selection frequencies, i.e., dominance
rates. Finally, smooth dominance rates are obtained via asmoothing operation, as depicted in Fig. 1 (d). A lowpass ﬁlter with a 1 Hz cutoff frequency was used in thisstudy, because at most one option was recorded during eachsecond within the period of high-frequency option selection
by panelists.

3

P RINCIPAL MOTION ANALYSIS (PMA) USING RE SAMPLED TD CURVES
3.1

Resampling of TD curves

Implementation of the TD method requires a sufﬁcient number of panelists to produce smooth dominance-rate proﬁles.
For example, in the experiment performed by Pineau etal. [3], 16 panelists participated in TDS tasks to generate
TD curves for each food product. In the study performedby Thomas et al. [24], 68 panelists signed up to facilitate
curve generation. Likewise, 20 (n = 20) panelists signed
up for participation in the experiments performed in thisstudy. A set of p different TD curve types for each productwas obtained by integrating the responses obtained fromthe n panelists. Treating this curve set as one sample, q such
samples were collected for q food products. In the present
case, q = 10. However, PMA in general requires collectionof more samples. Thus, to facilitate creation of additional
samples, the sampling exercise was repeated n times withsuccessive replacements from the pool of panelists, in accordance with the sampling method reported in [37], [38].
This process was performed n times to acquire n sets of TD
curves for each product. An aggregate of n×q sets of curves
was used for the computation of PMA, which is describedin the next section.
3.2 Principal motion analysis (PMA)
The principal motion analysis (PMA) technique was introduced by Park et al. [29], [30] as an extension of the principal
component analysis technique in the temporal dimension.
Thus far, PMA has been used for analyzing redundant systems, such as human motion [33], [34], [35]. The techniqueessentially computes several representative motions—called
principal motions—from a set of multivariate time-series
samples. These principal motions are independent fromeach other2 . A multivariate motion sample can be represented by a linear combination of principal motions. In
general, the number of meaningful principal motions issubstantially smaller compared to that of variables included
in original samples. All multivariate time-series samples are
ﬁt into a space with reduced dimensions.
(k)
In terms of the k -th sample, let xijt denote the dominance rate of the descriptor j at time t for food product i.

3(k)
The column vector xij contains discrete time values, andcan be expressed as
(k)

(k)

(k)

(k)

xij = (xij1 · · · xijt · · · xijs )T ∈ Rs×1

(1)

wherein temporally continuous dominance rates have been
discretized into s values within the normalized time domain
with t = 1, · · · , s. The conjunct column vector can be
expressed as
(k)

yi

(k)T

= (xi1(k)T· · · xij(k)T

· · · xip )T ∈ Rsp×1(2)

using which TD curves for p different types of descriptors
corresponding to the k -th sample of product i can be represented by a single vector. Conjunct vectors for all n samples
of product i can be consolidated in the following matrix
form.
(k)

(1)

Zi = (yi · · · yi(n)

· · · yi ) ∈ Rsp×n .

(3)

Subsequently, data corresponding to all products can be
expressed using an extended matrix as follows.

W = (Z1 · · · Zi · · · Zq ) ∈ Rsp×nq .

(4)Hence, q is the number of tasted food products.
Usually, W can be expanded using a set of independentvectors acquired by solving an eigenvalue expansion, andvectors with primary eigenvalues can subsequently be regarded as principal motion vectors. However, such vectors
may possess negative values, whereas dominance rates are
greater than zero in principle. Hence, a non-negative matrix
factorization [36] was performed in this study, using which
W could be expressed as

W ≈VH

(5)

wherein vectors V ∈ R
and H ∈ R
contained only
positive values. Here, r denotes the number of principal
motions to be considered, and each column vector of V
corresponds to a principal motion vector. The value of r
is, in general, small—3 in the case of the present study—
compared to the size of the matrix W . The vector H
contains scores corresponding to principal motion vectors.
(k)TD curves of an arbitrary sample yi can be expressed asa linear combination of r principal motion vectors weighedby scores contained within H ; i.e.,
sp×r

(k)

yi

r×nq

(k)

≈ V hi .

(6)(k)

In the above equation, hi ∈ Rr×1 denotes a column vectorof H . In other words, all samples were made to ﬁt within ther-dimensional space, each dimension of which corresponds
to one principal motion. As regards factorization of nonnegative matrices, the nnmf function within the MATLAB
package (ver. 2017b; MathWorks, Inc.) was used in thisstudy.

4

E XPERIMENTS

4.1 Panelists
1. As a variation of TD methods, multiple analog scales are used to
produce non-binary functions [12], [21].
2. This depends on the data processing method employed. In the caseof the present study, non-negative matrix factorization was performed
for matrix expansion, wherein principal motions were approximately
orthogonal to each other. If singular-value decomposition had been
used, the resulting principal motions would be completely orthogonal.

Twenty Japanese university students (age: 21–26, 16 male,
4 female, n = 20)3 participated in the TD tasks performed
3. There is no general agreement about the necessary number ofpanelists for TD methods. Typically, at least more than 10 panelists
are invited [31].

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019

in this study as panelists, after providing informed writtenconsent. All panelists were familiar with plum pickles as acommon food product.
4.2 Food products
Ten types of commercially available plum pickles, calledumeboshi in Japanese—(P1–P10, q = 10), were used in thisstudy. Umeboshi refers to the Japanese plum pickled withsalt. They are ﬂavored with Japanese red basil (shiso), bonito
soup, and so on. In the study, three umeboshi were honeyﬂavored (relatively sweet) and four were basil-ﬂavored(relatively sour), whereas the others were bonito-ﬂavoredwith moderate umami. During experiments performed inthis study, umeboshi pulp was torn into small pieces, andone piece was placed on a spoon and provided to a panelist
for tasting.
4.3 Selection of descriptors
During TD tasks, not more than ten descriptors must be
provided to panelists [19]. For descriptor determination, alist of 163 candidate descriptors was prepared for describing
sensory and affective experiences during eating. A team ofsix members, including the authors, ate the plum pickles
and selected possible descriptors from the prepared list in a
“check-all-that-apply” manner. Options that were voted formost often by the members were then selected as descriptors, such that their total count would not exceed 10 for eachcategory of sensory and affective experience.
Nine sensory descriptors—sour, sweet, umami, salty,smooth, refreshing, juicy, fruity, and watery—were selected.
Among these, sour, sweet, umami, and salty are fundamental
tastes [39], whereas watery, refreshing, and smoothing are
more concerned with texture, i.e., tactile sensations felt inthe mouth. Juicy can be described as the density of juice
extracted when a food sample is squeezed in the mouth.
Fruity is a comprehensive term, but it mostly relates to thesense of smell. Additionally, eight affective and evaluativedescriptors—like, dislike, delicious, expensive, rich, deep, sharp,
and arousing—were selected. Deep can be deﬁned as theintensity of multiple umami factors. Additionally, rich refers
to the variety of tastes experienced by the taster. Sharp canbe deﬁned as the rapid increase in stimulating tastes andﬂavors.
Thus, the number of descriptive words equaled p = 9
and 8 for the TDS and TDE tasks, respectively. These words
were displayed to the panelists in their native language, i.e.,
Japanese4 on the graphical user interface described in Fig. 1.
Either of the two groups of words—sensory or affective—
was presented during a single TD operation. Sensory andaffective descriptors were not mixed during the TD task.
Further, all descriptors were used during the SD task.
4.4 Sensory evaluation tasks (TD methods and aftertasting scoring method)
In this study, all 20 panelists performed the TD and aftertasting scoring tasks (SD method) in a random manner.
4. The corresponding Japanese sensory words were suppai, amai, umai,
shoppai, nameraka, sawayaka/sappari/assari, zyushi, furuthi, and mizuppoi
in the order of descriptors listed in the above passage. Likewise, thecorresponding affective words were suki, kirai, oishi, johinna/kokyuna,yutakana, ajiwai-fukai, surudoi, and mega-sameru.

4

Prior to the experiments, all panelists were briefed on thedescriptor deﬁnitions used in the two types of tasks.
4.4.1 TD tasks
Prior to performing the main tasks, panelists were asked toattend a 5–10-min training session to familiarize themselveswith the method and graphical user interface (GUI) setup.
During this session, panelists were asked to repeatedly
perform TD tasks using a set of sensory and affective
descriptors.
During each trial, panelists were asked to close theireyes, pinch their own nose, and place a food specimen in
their mouth using a disposable plastic spoon, on which apiece of pulp was placed by the experimenters. This wasdone to prevent visual and osmatic cues from affecting theexperiment. Immediately following food intake, panelists
opened their eyes and nose and proceeded with performing
the GUI-based option selection task. Once they felt a change
in experience, they selected a different option. On average,
each task lasted approximately 10 s. Prior to each trial,
panelists were asked to rinse their mouths.
Each panelist performed 20 trials (ten food products foreach of the TDS and TDE tasks). The order in which the
TDS and TDE tasks and products were presented to thepanelists was randomized. Note that TDS and TDE tasks
were separately conducted, following the methodology ofearlier studies such as [4], [22]. Further, the option labels
were randomly placed on the GUI panel for each panelist.
4.4.2 After-tasting semantic difference (SD) task
In this study, the panelists performed SD tasks using sevengrade scales for nine sensory and eight affective words written on the same questionnaire sheet. The phrases “strongly
agree” and “strongly disagree” represented the two extremes of each scale, while “neutral” represented the center.
In the analysis in section 4.5.2, positive and negative values
were assigned to “agree” and “disagree” grades, respectively.
Prior to execution of the main tasks, the panelists trained
themselves to score the food products as per the descriptive
norms provided. During this 5–10-min training session, thepanelists were allowed to eat all products used in the lattermain task.
In the main SD task, similar to the TD tasks, panelists
were asked to eat a piece of pickle placed on a spoon andsubsequently provide a response in accordance with theireating experience. Similar to the TD tasks, they could takeonly one piece of food for a single trial. Furthermore, theyclosed their eyes and nose before the intake of the food.
Once again, all food products were presented in a randomized order. Each panelist took approximately 2 min (onaverage) to respond to the questionnaire for each product.
4.5 Data analysis
4.5.1 Analysis of TD data
Binary functions corresponding to the nine (p = 9) sensory
and eight (p = 8) affective descriptors obtained during each
TD operation were normalized with respect to the timeat which the “stop” option was selected by each panelist.
Subsequently, resampling was performed to acquire TD

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019(a) TDS curves using sensory adjectives (U5)
Sour
SmoothSweet
Refreshing

Dominance rate

0.5

Juicy
FruityWatery5(c) TDS curves using sensory adjectives (U7)(b) TDE curves using affective/evaluative adjectives (U5)
Salty
Umami

Rich
Deep
Sharp
Arousing

Like
Delicious
Expensive
Dislike

0.5

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

0
0.2

0.4
0.6
Normalized time

0.8

1

Juicy
FruityWatery

Salty
Umami

0.2

0.4
0.6
Normalized time

0.8

0

0
0

Sour
SmoothSweet
Refreshing

0

0.2

0.4
0.6
Normalized time

0.8

1

0

1

Fig. 2. Example of TD curves obtained for food specimens U5 and U7—(a) TDS curves for sensory descriptors for specimen U5; (b) TDE curves
using affective/evaluative descriptors for specimen U5; (c) TDS curves using sensory descriptors for specimen U7. Normalized times 0 and 1
respectively indicate the commencement and end of an operation.
Sweet
Refreshing

Sour
SmoothLoadings of principal motion

1st principal motion (sensory)

Umami

WaterySalty

Juicy
Fruity2nd principal motion (sensory)

3rd principal motion (sensory)

0.1

0.1

0.1

0.05

0.05

0.05

0

0
0

Normalized time

0
0

1

1

Normalized time0

Normalized time1

Fig. 3. First three principal motions of sensory responses obtained using TDS data.

Loadings of principal motion

Like
Delicious

1st principal motion (affective)

Rich
Deep

Expensive
Dislike

Sharp
Arousing

2nd principal motion (affective)

3rd principal motion (affective)

0.1

0.1

0.1

0.05

0.05

0.05

0

0

0
0

Normalized time

10Normalized time

10Normalized time1

Fig. 4. First three principal motions of affective responses obtained using TDE data.

curves. Each continuous curve was split into 100 discrete
points (s = 100). Finally, matrices with orders of 900(p = 9 × s = 100) × 200 (n = 20 × q = 10) and 800(p = 8 × s = 100) × 200 corresponding to the sensory- andaffective-descriptor sets, respectively, were separately used
for PMA computation.
To compare PMA with representative analysis methods
employed in extant studies, so-called dominance durations
were also calculated using the original sample set. The dominance duration of a descriptor corresponds to the period
for which the descriptor remains selected. Each sample wascharacterized by a set of nine (TDS task) and eight (TDEtask) dominance durations. Principal component analysis(PCA) was then performed on 200 (10 products × 20 panelists) samples for each of the TDS and TDE tasks.
4.5.2

Analysis of after-tasting SD task

Responses obtained from each panelist were transformed
into z -scores for each descriptor. Thus, one sample obtained
from one panelist for one food product could be represented
as a vector comprising nine or eight normalized variables,

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019and 200 (10 products × 20 panelists) such vectors were used
to perform PCA.
In addition to the above-mentioned PCA computation
using normalized raw sample data, PCA was also performed after resampling to facilitate comparison against
PMA results. For each product, 20 vectors were sampledfrom the pool of the 20 panelists’ responses, with successive
replacements as described previously; subsequently, they
were averaged to acquire a new sample vector. This process
was repeated to prepare 20 new samples corresponding toeach of the ten products. The 200 samples, thus prepared,
were used to perform PCA computations.
4.5.3 Multivariate analysis of variance (MANOVA) for discriminating products
Food products considered in this study were discriminatedby comparing their principal motion and principal component scores acquired, respectively, from the TD and aftertasting scoring tasks. To this end, multivariate analysis ofvariance (MANOVA) was used to compare two arbitrary
products, each of which was characterized using multiple
variables. Two major principal motions and components
were employed because two is the number often adopted
to represent samples on a planar sheet, and all the productpairs were discriminated by using only two principal motions computed from the TD results. Hence, each productwas represented by two principal scores for either of thetwo tasks, meaning that the number of multivariate for
MANOVA was two. One MANOVA test compared two
products, each of which consisted of twenty samples. This
was true for TD and SD tasks, and the numbers of samples
used in the test were the same for all the combinations oftasks and descriptive word sets. This test was performed on
all pairs prepared using the 10 products—45 pairs (10 P2 ) in
total. When the p-value of the test for a pair was observed tobe less than 0.05, that pair was judged to be discriminated.
The p-value was not corrected for multiple tests.

5

R ESULTS

5.1 TD-curve examples
Fig. 2 depicts TD curves computed using results obtained from panelist responses for two products—U5: basilﬂavored and U7: honey-ﬂavored—considered as examples.
As regards product U5, sour and salty tastes were salient,and selection of these responses was observed to rapidly
increase immediately after intake of product U5 (Fig. 2(a)). Subsequently, responses corresponding to the sour experience became moderate, whereas those corresponding
to intense salty experiences lasted until the end of theoperation. Along with these two salient sensations, highvalues were recorded for arousing and sharp, both of which
are stimulating experiences (Fig. 2 (b)). During the second
half of the operation, responses corresponding to experiences dislike and deep gradually increased in number. In
contrast, taste responses observed for the honey-ﬂavored
food specimen (U7) were sweet for the most part (Fig. 2 (c)).
In particular, during the early phase, the sensory experience
was rated to be fruity by most panelists. During the lastphase, corresponding responses for U7 were observed to

6change to sweet, smooth, and refreshing. Thus, temporal sensory and affective responses concerning the two products
demonstrated substantial change.
It is to be noted that during the very early phase of an
operation, the sum of all response curves did not equal 1.0
because panelists were still indecisive about their dominant
experiences, and thus, no options were selected during suchphases. This phenomenon can be explained in terms of thedelay in gustatory detection and identiﬁcation [40].
5.2

Principal motions and scoresFigs. 3 and 4 depict examples of principal motions represented as TDS and TDE curves. Although only the ﬁrst
and second principal motions were considered during the
product discrimination described in section 5.4, motions 1–
3 have been depicted in these ﬁgures. Note that unlike PCA,
the computation of contribution ratios does not hold for
non-negative matrix factorization.
Primary motions concerning sensory descriptors are
depicted in Fig. 3. As can be observed, the ﬁrst motion
was clearly featured by sour and salty responses. The sour
response was observed to be more dominant during theearly phase, whereas the salty response remained dominant
until the end of the operation. The second motion wasfeatured by responses sweet and juicy. During the last phase,the refreshing response was observed to gradually become
dominant. The third principal motion can be described by
the sour response during the early phase, and this wasobserved to change to umami during the second half of theoperation.
Additionally, principal motions of affective responses are
depicted in Fig. 4. The ﬁrst motion mainly comprised positive experiences such as like and delicious. These responses
seem to be strongly correlated. During the early phase,
deep and rich responses were observed to be moderately
dominant. The second motion was featured by the sharp andarousing responses during the early phase. Subsequently, thedeep response was observed to gradually increase until theend of the operation. The third motion was featured by
the rich, deep, and expensive responses, which correspond topositive feelings. However, interestingly, the third motion
did not include dominant like and delicious responses.
Fig. 5 depicts distribution examples concerning the ten
types of plum pickles on principal motion planes. For eachproduct, the centroid and standard deviation of 20 samples
prepared using the resampling method have been depicted.
On the plane of sensory descriptors, basil-ﬂavored pickles,
such as samples U3, U5, and U10, exhibited high scores
corresponding to the ﬁrst principal motion featuring sourand salty responses. In contrast, honey-ﬂavored pickles exhibited large scores corresponding to the second principal
motion featuring sweet and juicy responses. Bonito-ﬂavored
pickles exhibited small ﬁrst- and second-principal motion
scores; however, large scores corresponding to the thirdprincipal motion were observed in this case, as describedby the central plot in Fig. 5. The third motion demonstrated
intense umami responses.
On the plane of affective descriptors, two honey-ﬂavored
pickles (U4 and U8) and one basil-ﬂavored pickle (U9) wereobserved to demonstrate large scores corresponding to theJOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 20197

[Sensory (TDS, 1st-2nd plane)]

Bonito-flavored

2
U4

U9
U2

0

U2

U5

2

U1
U3
U9

U10

U7

U10

2

U5
U3
U1

U8

0

U4

U6

U2

U9

U7

0
5

First principal motion(Sour and salty)

U8

U4

U5

U10

U6

0

U3

U1

Second principal motion(Sharp, arousing, and deep)

U6

Basil-flavored
U8

4

4

Honey-flavored

U7

Third principal motion(Umami and sour)

Second principal motion(Sweet and smooth)4[Affective (TDE, 1st-2nd plane)][Sensory (TDS, 2nd-3rd plane)]044First principal motion(Deep, like, and delicious)

Second principal motion(Sweet and smooth)Fig. 5. Distribution of food samples on principal motion planes (TDS data). Dots denote centroids of samples, whereas the spread of ellipses
denotes the region of inﬂuence of standard deviations.

Sour
0

U5

U2
U6
U10
U9
U1
U3

Fruity

U8
U4

-4

4

Sweet

U7

0

Basil-flavored
Bonito-flavored

Juicy
U1
U8 U6
U2
U9
U3
U10
U5
U4
U7

Sharp
Arousing

Expensive

Salty
Sour
Smooth4Second component

Juicy[Affective (after-tasting SD)]

Umami

Third component

Second component

4[Sensory (after-tasting SD), 2nd-3rd plane][Sensory (after-tasting SD), 1st-2nd plane]
WaterySmoothSalty

Deep
Delicious
Like

0
U8

U2
Ő
U6

U3 U5
U9

U1

U10
Dislike

U7-4

U4

Refreshing Watery

Honey-flavored
-4

-4

0
First component

40
4Second component-40First component

4

Fig. 6. Distribution of food samples on principal component planes computed by using ratings from SD tasks. Dots denote centroids of samples,
whereas the spread of ellipses denotes the region of inﬂuence of standard deviations. Note that only those descriptors with large loadings havebeen considered for inclusion in this ﬁgure. Centroids of specimens U2 and U6 can be observed to be largely overlapped in the leftmost plot.

ﬁrst principal motion. As depicted in Fig. 4, this motion
was featured by a large number of deep, like, and delicious
responses. One basil-ﬂavored product (P10) exhibited large
scores in terms of the second principal motion, including
sharp and arousing responses. Food specimens, such as U1and U2, demonstrating small scores for both the ﬁrst- andsecond-principal motions exhibited large scores corresponding to the third-principal motion that featured rich and deep
responses.

5.3 Principal component scores corresponding toafter-tasting SD tasks
Fig. 6 depicts product distributions on principal component
planes computed based on the responses obtained during
the after-tasting SD task. Within the sensory descriptor
space, contribution ratios for the ﬁrst three principal components were observed to be 0.40, 0.17, and 0.11, respectively.
The ﬁrst component comprised three responses—sweet, sour,and salty. Honey-ﬂavored pickles that were relatively sweet
were placed within the positive region of the ﬁrst component. In contrast, basil-ﬂavored pickles with intense sourness were placed within the negative region. The second
component that reﬂected the quality of plum pulps featured

watery and smooth responses. The third component comprised umami responses, as described by the central plot
in Fig. 6. Bonito-ﬂavored products were placed within thepositive region of the third component.
Contribution ratios concerning major principal components of affective responses obtained during the SD task
equaled 0.48, 0.30, and 0.08. The ﬁrst component was clearly
represented by hedonic aspects, such as like, delicious, anddislike. Deep and expensive responses were also observed
to align with the ﬁrst component. The second component
strongly featured the sharp and arousing responses. These
affective responses were observed in combination with thesour and salty sensory responses. It can therefore be inferredthat experiences of sharpness and arousal can be caused bysalty and sour tastes.
When the resampling method was used during PCA,
corresponding results were observed to be largely similar
to those obtained using raw samples. No substantial differences were observed in dimensional structures, product allocation within spaces, or contribution ratios corresponding
to major components.

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019

8

TABLE 1
Number of product pairs discriminated by MANOVA. Forty-ﬁve pairs
made of the 10 products were tested. For the methods using
resampling, the values are means of 10 repetitions.

Sensory
Affective

5.4

After-tasting SD task
Normal Resampled
PCA
PCA
30
32.2
24
26.7

TD tasks
PCA of dominance durations
28
22

PMA
45.0
44.4

Statistical discrimination of products

Table 1 lists the number of statistically discriminated pairs
prepared from among the ten products using the principal
component and motion scores exclusively. Products werediscriminated based on the planes of ﬁrst-second principal
components or motions. During implementation of the resampling method, sample sets were randomly produced. Insuch cases, therefore, resampling and test procedures wererepeated ten times each, and corresponding mean values
have been listed in Table 1. The differences between resampled pools were observed to demonstrate little impact on thediscriminative performance. The number of discriminatedpairs varied at most by 1 during the ten repetitions.
PMA results were observed to demonstrate the highest
discrimination performance. In particular, when using TDS
curves, discrimination of all products could be realized.
Principal motion scores concerning affective responses werealso highly discriminable. As depicted in Fig. 5, standarddeviation ellipsoids of products barely overlapped one another, and these can, therefore, be easily discriminated.
Interestingly, discriminative performances observed via
normal PCA of data obtained during after-tasting SD tasks
were found comparable to those observed via PCA of TD
curves’ dominance durations. Nonetheless, the number of
discriminable pairs were of the order of 28–30 (62–67% of
all pairs) and 22–24 (49–53%), corresponding to the sensory
and affective descriptors, respectively. These numbers arevery small compared to PMA, wherein discrimination ofnearly all pairs could be realized.
As regards after-tasting SD tasks, use of the resamplingtechnique slightly increased the number of discriminable
pairs by two or three. It is to be noted that the number ofsamples analyzed was not increased by resampling, and thatthe degrees of freedom of MANOVA were identical under
all test conditions.

6

D ISCUSSION

6.1

Semantic validity of PMA and PCA

As described in sections 5.2 and 5.3, respectively, on theprincipal-motion and principal-component planes, the allocations of products were reasonable considering the nominal ﬂavors of products. Further, this section discusses
the semantic consistency of results obtained using the TD
method and performing after-tasting SD tasks. Some extant research efforts [18], [21], [23] have reported similar
conclusions based on comparisons of results obtained via

principal component analyses5 of static sensory evaluation
and TD methods [18], [21], [23]. Consequently, agreement
between the two methods can be considered to corroborate
the validity of experiments and analyses performed in thisstudy.
As regards sensory responses, dimensional structures
corresponding to the principal motion and component
spaces did not demonstrate a perfect match. In the principal
motion space depicted in Fig. 5, dimensions corresponding
to the sour and salty responses (ﬁrst dimension) were observed to be perpendicular to those corresponding to thesweet and smooth responses (second dimension). In contrast,in the principal component space depicted in Fig. 6, vectors corresponding to the sour and salty responses wereobserved to be directed opposite to the sweet response.
They were perpendicular to those corresponding to thesmooth responses. These differences were primarily causedby differences in the polarity of TD and SD tasks. TD tasksdeal with fully unipolar properties, because all panelist
response options considered in this study demonstrated
only two states—selected or unselected, and non-negative
matrix factorization produces only positive values. During
SD tasks, however, each adjective demonstrated bipolarstates—agree, neutral, and disagree. Hence, within the principal component space of SD tasks, the response opposite
to salty and sour can semantically be considered to be sweet,
which is psychologically correct [42]. Nonetheless, considering the gustatory system, wherein these three taste types
are considered fundamental and independent [39], the sweet
response is independent of the sour and salty responses. In
this regard, the principal motion space is biologically sound.
With regard to affective responses, as depicted in Fig. 5(right), food specimens U4, U8, and U9 exhibited largeﬁrst-motion scores, whereas specimens U3, U5, and U10
demonstrated low scores. The ﬁrst motion was characterized by responses deep, like, and delicious. As a result, foodspecimens U4, U8, and U9 were preferred over U3, U5, andU10. Likewise, on the right plot depicted in Fig. 6, foodspecimens U3, U5, and U10 were clearly disliked, whereas
specimen U8 demonstrated large scores corresponding to
the like and delicious responses. Specimens U4 and U9 wererather neutral in terms of likeability, and were, therefore,
placed between vectors corresponding to the like and dislike
responses. Further, as depicted in Fig. 6 (right), vectors corresponding to responses like, delicious, and deep demonstrate
similar directions, which implies existence of a correlation
between these responses during SD tasks. This demonstrates good agreement with results obtained for the ﬁrstprincipal motion using TDS curves exhibiting existence of acoupling between salient responses.
The dimensionality of the two affective spaces demonstrated good agreement with each other. In the TDE space
depicted in Fig. 5 (right plot), the ﬁrst dimension characterized by responses like and delicious was observed to
be orthogonal to the second dimension characterized byresponses sharp and arousing. Deep responses were included
5. They used canonical variates analysis (CVA), which is a variationof PCA. PCA and CVA result in similar conclusions with regard tosensory analysis data [41]. Furthermore, PCA tends to discriminate agreater number of product pairs compared to CVA, which is contradictory to intuition [41].

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019within both dimensions. This was also true within the spacecalculated using data obtained via SD tasks, as depicted
in Fig. 6 (right plot). The like-dislike line was observed to
be orthogonal to vectors for sharp and arousing. Further,
the vector corresponding to the deep response was locatedbetween the two dimensions.
Overall, the principal-motion and principal-component
spaces computed using the TD- and after-tasting SD-task
data, respectively, did not include substantial inconsistencies.
6.2 Advantage of TD-PMA approach over conventional
SD method
As described in Table 1, the TD method was observed todiscriminate nearly all pairs of the ten food products. In
contrast, the after-tasting SD method could discriminate
only 30 and 24 product pairs (out of 45) corresponding
to the sensory and affective descriptors, respectively. TD
data contain information related to the temporal evolutionof descriptors, and are richer compared to data acquired
using the SD method. Thus, when considering time-series
properties, the discrimination performance of food products
is highly enhanced. The PMA approach facilitates performance of such statistical analyses of multivariate time-series
information. Nonetheless, it must be noted that comparedto TD methods, SD methods usually require panelists withmore training, and that in this study, only university students were recruited instead of professional panelists.
PCA scores computed using dominance durations as
variables were comparable or slightly inferior to those computed using results obtained via SD tasks in terms of product discrimination performance. This does not immediately
indicate that the use of dominance durations is not recommended; however, their use also does not offer signiﬁcantadvantages over conventional scoring tasks. This is intuitively reasonable, because dominance durations are basedon the most dominant experience being recorded at each instant without involving use of other information. For certain
food products considered in this experiment, the dominanceduration values of certain descriptors nearly equaled zero.
For example, the watery response was rarely observed to become dominant throughout the experiment, and the corresponding dominance duration values remained nearly zero.
During the scoring task, however, scores corresponding to
the watery response varied among products, and therefore,
could be used for product categorization.
6.3 Limitations of research
We resampled the results of twenty panelists in order toproduce smooth TD curves for computing PMA. Without
resampling, a large number of panelists would be required,
which is costly and impractical for industrial purposes, oreach panelist would need to replicate TD tasks multiple
times, possibly more than ten, for the same food productto produce smooth TD curves. Such replication was not
performed in earlier studies, potentially owing to limitations
in availability of edible items. Hence, we consider that PMA
and resampling of TD data are inseparable. Nonetheless,it remains unclear whether resampling causes any adverse
effects on the conclusions. One concern is that resampling

9

may enhance product discrimination as seen in the case of
our PCA plus SD method. A noticeable warning at this pointis that we should not conduct resampling based on the small
original sample pool.
In addition, affective responses are generally highly individual, and this aspect has been extensively investigatedin extant literature [43], [44], [45], [46], [47]. However, TDbased methods are not well compatible with individual
analysis. When employing TD methods, responses fromindividual panelists are aggregated to produce a set ofsmooth TD curves. Hence, it automatically addresses average properties among panelists. Generation of TD curves
for individual panelists requires a large number of repetitions per panelist; however, multiple repetitions of the same
stimulus by the same panelist may lead to unknown problems with regard to data analysis. The proposed method is
unsuitable with regard to performing individual analysis,
owing to the resampling of new sample sets from originalones. In contrast, static sensory evaluation methods, suchas the semantic differential technique and sensory proﬁling,facilitate analysis of individual differences. From this aspect,
TD methods may as well be used in conjunction with static
methods, as suggested in [21]. Further, they must be complementary in terms of the number of descriptive norms; i.e.,
the number of descriptors used during a single operation of
a TD task must be less than ten [19] to avoid overloadingof panelists. In contrast, more descriptors can be used when
implementing static methods.

7

C ONCLUSIONSThis study demonstrates successful validation of principalmotion analysis (PMA) of TD data concerning temporalchanges in multiple sensory and affective experiences ofrecruited panelists for tasting of food products. As observed, PMA linearly decomposes multivariate time-series
data into a small number of principal motions that are easilyinterpretable and common among all samples. Using twoscores, each of which corresponded to the ﬁrst- and secondprincipal motions, pairs of ten plum pickles were judged
to be different with near perfection when pre-prepared setsof sensory or affective descriptors were used during the
TD task. In contrast, results obtained through the semantic
differential (SD) method—performed as representative of aconventional static method—demonstrated discriminationof only 30 and 24 pairs (out of 45 each) using sensory
and affective descriptors, respectively. Results obtained via
PCA and PMA using SD and TD data, respectively, wereobserved to be semantically consistent. It can, therefore,
be concluded that PMA is suitable for use in conjunction
with the TD method, especially with regard to productdiscrimination.

ACKNOWLEDGMENTS
This study was supported in part by MEXT Kakenhi(#17K20002).

R EFERENCES[1]

R. Cowie, G. McKeown, and E. Douglas-Cowie, “Tracing emotion:
an overview,” International Journal of Synthetic Emotions, vol. 3,
no. 1, pp. 1–17, 2012.

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019[2]

W. E. Lee III and M. Pangborn, “Time-intensity: the temporal
aspects of sensory perception,” Food technology, pp. 71–78, 1986.
[3] N. Pineau, P. Schlich, S. Cordelle, C. Mathonnière, S. Issanchou,
A. Imbert, M. Rogeaux, P. Etiévant, and E. Köster, “Temporal
dominance of sensations: Construction of the tds curves and comparison with time–intensity,” Food Quality and Preference, vol. 20,
no. 6, pp. 450–455, 2009.
[4] G. Jager, P. Schlich, I. Tijssen, J. Yao, M. Visalli, C. De Graaf,and M. Stieger, “Temporal dominance of emotions: Measuring
dynamics of food-related emotions during consumption,” Food
Quality and Preference, vol. 37, pp. 87–99, 2014.
[5] N. Pineau and P. Schilch, “Temporal dominance of sensations(TDS) as a sensory proﬁling technique,” in Rapid Sensory Proﬁling
Techniques. Elsevier, 2015, pp. 269–306.
[6] L. M. Duizer, K. Bloom, and C. J. Findlay, “Dual-attribute timeintensity measurement of sweetness and peppermint perceptionof chewing-gum,” Journal of Food Science, vol. 61, no. 3, pp. 636–
638, 1997.
[7] S.-M. Miettinen, L. Hyvönen, R. S. T. Linforth, A. J. Taylor,and H. Tuorila, “Temporal aroma delivery from milk systems
containing 0–5% added fat, observed by free choice proﬁling,time intensity, and atmospheric pressure chemical ionization-mass
spectrometry techniques,” Journal of Agricultural and Food Chemistry, vol. 52, no. 26, pp. 8111–8118, 2004.
[8] D. W. Ningtyas, B. Bhandari, N. Bansal, and S. Prakash, “Sequential aspects of cream cheese texture perception using temporal
dominance of sensations (TDS) tool and its relation with ﬂow andlubrication behaviour,” Food Research International, 2018.
[9] M. Sokolowsky and U. Fischer, “Evaluation of bitterness in whitewine applying descriptive analysis, time-intensity analysis, andtemporal dominance of sensations analysis,” Analytica Chimica
Acta, vol. 732, pp. 46 – 52, 2012, a selection of papers presented
at In Vino Analytica Scientia.
[10] “Case study 6 - a temporal dominance of sensation (tds) analysisof three winesa,” in Sensory and Instrumental Evaluation of Alcoholic
Beverages, H. Heymann and S. E. Ebeler, Eds. San Diego: Academic Press, 2017, pp. 198 – 204.
[11] T. Okada, S. Okamoto, Y. Yamada, and T. Ishikawa, “Vector autoregression model of temporal perceptual and affective responses
towards food,” in Proceedings of IEEE Global Conference on Life
Sciences and Technologies, 2019, pp. 43–45.
[12] M. Ng, J. Lawlor, S. Chandra, C. Chaya, L. Hewson, and J. Hort,“Using quantitative descriptive analysis and temporal dominanceof sensations analysis as complementary methods for proﬁling
commercial blackcurrant squashes,” Food Quality and Preference,
vol. 25, no. 2, pp. 121 – 134, 2012.
[13] Y. Ebara, S. Okamoto, T. Okada, and Y. Yamada, “Comparison of
the method of temporal dominance of sensations and after-tasting
sensory evaluation,” in Proceedings of IEEE Global Conference on Life
Sciences and Technologies, 2019, pp. 210–211.
[14] P. Varela, L. Antúnez, M. Carlehög, F. Alcaire, J. C. Castura,
I. Berget, A. Giménez, T. Næs, and G. Ares, “What is dominance?
An exploration of the concept in TDS tests with trained assessors
and consumers,” Food Quality and Preference, vol. 64, pp. 72–81,
2018.
[15] J. F. Rodrigues, V. R. de Souza, R. R. Lima, J. ao de Deus
Souza Carneiro, C. A. Nunes, and A. C. M. Pinheiro, “Temporal
dominance of sensations (TDS) panel behavior: A preliminary
study with chocolate,” Food Quality and Preference, vol. 54, pp. 51 –57, 2016.
[16] G. Ares, S. R. Jaeger, L. Antúnez, L. Vidal, A. Giménez, B. Coste,
A. Picallo, and J. C. Castura, “Comparison of tcata and tds for
dynamic sensory characterization of food products,” Food ResearchInternational, vol. 78, pp. 148–158, 2015.
[17] Q. C. Nguyen, T. Næs, and P. Varela, “When the choice of thetemporal method does make a difference: TCATA, TDS and TDS
by modality for characterizing semi-solid foods,” Food Quality andPreference, vol. 66, pp. 95–106, 2018.
[18] A. Saint-Eve, I. Déléris, M. Panouillé, F. Dakowski, S. Cordelle,
P. Schlich, and I. Souchon, “How texture inﬂuences aroma andtaste perception over time in candies,” Chemosensory Perception,
vol. 4, no. 1, p. 32, Apr 2011.
[19] N. Pineau, A. G. de Bouillé, M. Lepage, F. Lenfant, P. Schlich,
N. Martin, and A. Rytz, “Temporal dominance of sensations: Whatis a good attribute list?” Food quality and preference, vol. 26, no. 2,
pp. 159–165, 2012.

10[20] M. Lepage, T. Neville, A. Rytz, P. Schlich, N. Martin, and
N. Pineau, “Panel performance for temporal dominance of sensations,” Food Quality and Preference, vol. 38, pp. 24 – 29, 2014.
[21] “Temporal dominance of sensations and sensory proﬁling: A
comparative study,” Food Quality and Preference, vol. 20, no. 3, pp.
216 – 221, 2009.
[22] R. van Bommel, M. Stieger, P. Schlich, and G. Jager, “Dutch
consumers do not hesitate: Capturing implicit ’no dominance’ durations using hold-down temporal dominance methodologies for
sensations (TDS) and emotions (TDE),” Food Quality and Preference,
vol. 71, pp. 332 – 342, 2019.
[23] A. Albert, A. Salvador, P. Schlich, and S. Fiszman, “Comparison between temporal dominance of sensations (TDS) and keyattribute sensory proﬁling for evaluating solid food with contrasting textural layers: Fish sticks,” Food Quality and Preference, vol. 24,
no. 1, pp. 111 – 118, 2012.
[24] A. Thomas, M. Visalli, S. Cordelle, and P. Schlich, “Temporal
drivers of liking,” Food Quality and Preference, vol. 40, pp. 365–375,
2015.
[25] M. Sokolowsky, A. Rosenberger, and U. Fischer, “Sensory impact
of skin contact on white wines characterized by descriptive analysis, time-intensity analysis and temporal dominance of sensations
analysis,” Food Quality and Preference, vol. 39, pp. 285–297, 2015.
[26] C. Dinnella, C. Masi, G. Zoboli, and E. Monteleone, “Sensory
functionality of extra-virgin olive oil in vegetable foods assessedby temporal dominance of sensations and descriptive analysis,”
Food Quality and Preference, vol. 26, no. 2, pp. 141 – 150, 2012.
[27] B. C. Franczak, R. P. Browne, P. D. McNicholas, J. C. Castura,and C. J. Findlay, “A markov model for temporal dominanceof sensations (tds) data,” in Proceedings of 11th Pangborn Sensory
Science Symposium, Gothenburg, Sweden, 2015.
[28] G. Lecuelle, M. Visalli, H. Cardot, and P. Schlich, “Modelingtemporal dominance of sensations with semi-markov chains,”
Food Quality and Preference, vol. 67, pp. 59 – 66, 2018.
[29] F. C. Park and K. Jo, “Movement primitives and principal component analysis,” in On Advances in Robot Kinematics, J. Lenarčič
and C. Galletti, Eds. Dordrecht: Springer Netherlands, 2004, pp.
421–430.
[30] B. Lim, S. Ra, and F. C. Park, “Movement primitives, principal
component analysis, and the efﬁcient generation of natural motions,” in Proceedings of the 2005 IEEE International Conference on
Robotics and Automation, 2005, pp. 4630–4635.
[31] R. Di Monaco, “Temporal dominance of sensations: A review,”
Trends in Food Science & Technology, vol. 38, no. 2, pp. 104 – 112,
2014.
[32] P. Schlich, “Temporal dominance of sensations (TDS): A new deal
for temporal sensory analysis,” Current Opinion in Food Science,
vol. 15, pp. 38–42, 2017.
[33] K. Mishima, S. Kanata, H. Nakanishi, Y. Horiguchi, and
T. Sawaragi, “Extraction of similarities and differences in humanbehavior using singular value decomposition,” Proceedings of theJapan Joint Automatic Control Conference, vol. 52, pp. 39–44, 2009.
[34] J. Aleotti, A. Cionini, L. Fontanili, and S. Caselli, “Arm gesturerecognition and humanoid imitation using functional principal
component analysis,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, pp. 3752–3758.
[35] N. Yamada, S. Okamoto, H. Okumura, Y. Yamada, K. Isogai, and
K. Kawakami, “Similarities and differences in manual stretchingof physical therapists for equinovarus,” Proceedings of IEEE/SICE
International Symposium on System Integration, pp. 490–495, 2014.
[36] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix
factorization,” in Proceedings of the 13th International Conference on
Neural Information Processing Systems, ser. NIPS’00. MIT Press,
2000, pp. 535–541.
[37] B. Efron, “Bootstrap methods: Another look at the jackknife,” The
Annals of Statistics, vol. 7, no. 1, pp. 1–26, 1979.
[38] B. Efron and R. J. Tibshirani, An Introduction to the Bootstrap.
Chapman & Hall, 1993.
[39] B. Lindemann, “Taste reception,” Physiological Reviews, vol. 76,
no. 3, pp. 719–766, 1996.
[40] R. Wallroth and K. Ohla, “As soon as you taste it: Evidence
for sequential and parallel processing of gustatory information,”
eNeuro, vol. 5, no. 5, pp. e0269–18.2018, 2018.
[41] C. Peltier, M. Visalli, and P. Schlich, “Comparison of canonicalvariate analysis and principal component analysis on 422 descriptive sensory studies,” Food Quality and Preference, vol. 40, pp. 326–
333, 2015.

JOURNAL OF LATEX CLASS FILES, VOL. X, NO. X, APRIL 2019[42] S. S. Schiffman and R. P. Erickson, “A psychophysical model forgustatory quality,” Physiology and Behavior, vol. 7, pp. 617–633,
1971.
[43] S. Okamoto, H. Nagano, K. Kidoma, and Y. Yamada, “Speciﬁcation of individuality in causal relationships among texturerelated attributes, emotions, and preferences,” International Journalof Affective Engineering, vol. 15, no. 1, pp. 11–19, 2016.
[44] A. Klöcker, C. Arnould, M. Penta, and J.-L. Thonnard, “Rasch-built
measure of pleasant touch through active ﬁngertip explorations,”
Frontiers in Neurorobotics, vol. 6, no. 5, 2012.
[45] I. Soufﬂet, M. Calonnier, and C. Dacremont, “A comparison between industrial experts’ and novices’ haptic perceptual organization: a tool to identify descriptors of the handle of fabrics,” Food
Quality and Preference, vol. 15, pp. 689–699, 2004.
[46] T. Ishikawa, S. Nakamori, K. Sasaki, K. Miyatake, and M. Ayama,“Investigation of appearance and tactile sensation scales for evaluating clothes in online shopping,” Proceedings of the International
Conference on Kansei Engineering and Emotion Research, pp. 537–543,
2014.
[47] T. Chamorro-Premuzic, C. Burke, A. Hsu, and V. Swami, “Personality predictors of artistic preferences as a function of the emotional valence and perceived complexity of paintings,” Psychologyof Aesthetics Creativity and the Arts, vol. 4, no. 4, pp. 196–204, 2010.

Shogo Okamoto received his Ph.D. in Information Sciences from Tohoku University in 2010.
He has been with Nagoya University since 2010.
Currently, he is an associate professor at the
Department of Mechanical Systems Engineering, Nagoya University. His research interests
include haptics, assistive robotics, and affectiveengineering.

Yuki Ehara is a mechanical engineer who received his B.S. in engineering from Nagoya University in 2019. His research interests include
affective science and engineering.

Takumu Okada received his B.S. and M.S. inmechanical engineering from Nagoya University
in 2017 and 2019, respectively. He is a mechanical engineer with the research interests inhaptics.

Yoji Yamada received his Ph.D. from Tokyo
Institute of Technology in 1990. He had beenwith Toyota Technological Institute since 1983.
In 2004, he joined the Intelligent Systems Research Institute of AIST as a group leader. In
2009, he moved to Nagoya University as aprofessor. His expertise includes safety of machines, intelligent technology in human/machine
systems, and robotic sensing and control.

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