ISASE 2023

Principal Component Analysis of Time Series Taste Datato Classify Processed HamMayu HARIU*, Shogo OKAMOTO*,†, Hiroharu NATSUME,* and Takuya DOI*
* Tokyo Metropolitan University, 6-6, Asahigaoka, Hino, Tokyo, Japan† Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan

Abstract: Temporal dominance of sensations (TDS) is a sensory evaluation method that measures changes in several types ofsensations over time during food sample tasting. Previous analytical methods for TDS do not classify different foods using the dynamicsensory information of the TDS method results. We developed a new approach to classify products by principal component analysis
computation of the time-series information of sensory responses. This method could classify five different hams in a two-dimensional
principal component space. The time-series information possessed by each dimension was interpretable, which suggests that thedeveloped method is helpful for analyzing the temporal properties captured by TDS methods.
Keywords: Temporal dominance of sensations, Principal component analysis, Hamapproach computes the principal component space basedon the discrete time-series results of the TDS tasks. We
then applied this method to separate the results of the TDS
method for five different brands of processed ham.
This study was approved by the Institutional Review
Board of the Faculty of Engineering, Nagoya University(#20-20).

1. INTRODUCTION
Temporal dominance of sensations (TDS) is a type ofdynamic sensory evaluation [1]. It enables recording oftime-series changes in multiple types of sensations while
a human is experiencing a stimulus. This revolutionary
method has been widely used over the last decade in thefield of food science. Researchers in other fields,including affective engineering, have recognized it as aneffective approach for capturing sensory dynamics [2, 3].
The most significant feature of the TDS method is therecording of the time evolution of sensory responses.
However, major analysis methods do not make use of such
dynamic properties. For example, ISO 13299 [1]standardized methods for visualizing the results of TDS
methods, but no statistical analyses were based on thedynamic data acquired using the TDS method. Dynamicanalyses were developed in some studies [4–6], wherecausality analysis [4], short-memory model [5], and semiMarkov model [6] of TDS data were introduced. Theseearlier studies mathematically modelled the time-series
sensory responses of humans. In contrast, the present
study developed a method to segregate different foodproducts using the time-series data.
We classified several brands of a food product in a
principal component space, which is suitable for productclassification, because food samples are localized withlittle effect of accidental or noisy variation in theresponses of sensory evaluation tasks. However, earlier
approaches computed space based on non-time-series
information calculated from the results of TDS tasks [7,8]. For example, the maximum response value during theentire task period was used in one study [7]. The present

2. METHODS
2.1 TDS tasks on five types of processed ham
In TDS tasks, a graphical user interface is used, as
shown in Figure 1. Several buttons labeled with gustatory,
olfactory, and textural attributes are arranged. A panelist
places a food piece in his/her mouth and presses a button
that best explains the sensation that is dominantly felt.
This button selection was repeated until the panelist
swallowed food. At any instant, only one button can beselected and the button is selected until the next button ispressed. Eleven types of attributes were used: juicy, tender,
smoky, umami, salty, fatty, sweet, elastic, dry, fibrous,and fragile.
We used five types of processed ham sold at
supermarkets in Japan. Each ham was cut into 3-cm
squares and presented to the panelists. Food samples werestored at room temperature (approximately 20oC). Eachpanelist tested each type of ham five times in random
order. Prior to each tasting task, the panelist rinsed his/hermouth with water.
2.2 Panelists
Eleven university students in their 20s participated in
the tasks after providing informed consent.

1sets of TDS curves that were obtained by bootstrap
resampling. The interval from the start to the end of thetask was divided into ten equal parts, and the last value of
the TDS curve in each part was used for the analysis.
Hence, each TDS curve set consisted of 110 variables (11types of attributes × 10 discrete points).
3. RESULTS
Figure 3 shows scatter plots of the resampled TDS
curves for the first and second principal components. Eachmark represents a different ham product. Different typesof ham formed different clusters in the principal
component space and five types of ham weredistinguished. The first principal component alone did notclassify hams C, D, or E. However, the second principal
component clearly distinguished them.
The principal component coefficients of the attributes
are shown in Figure 4 (a) and (b). Here, only prominent
positive and negative attributes are shown. In these figures,
dots for the same attributes are connected by lines. A
positive coefficient for an attribute at a given time pointmeans that its dominance proportion was higher than its
average among all hams. A negative coefficient indicatesthat it is less dominant than average. Attributes not drawn
in the figures are nearly the average of all hams.

Figure 1 Button panel used for TDS task [9]JuicyTenderDry

Dominance Proportion

0.3

Smoky
Umami
FibrousSalty
Fatty
FragileSweetElasticChance

0.2

0.1

000.2

0.40.6
Normalized Time (-)

0.8

1.0

Figure 2 TDS curves of Ham C. Temporal evolutionof proportions at which each attribute isdominantly perceived. Chance level is 0.09.

4. DISCUSSIONAs shown in the scatter plots in Figure 3, the five typesof ham were classified using only two principalcomponents. Rodrigues et al. attempted to classify threetypes of chocolate using principal component analysis
based on the results of TDS tasks [7]. They did not use thetime-series information of the TDS curves but only threeparameters for each attribute, including the maximum
value of the curve, for their principal component analysis.
The three types of chocolate were not classified in a twoprincipal component plane. Although we cannot simply
compare this study with [7], our results suggest theusefulness of a classification method that utilizes the timeseries information of the TDS curves.
Here, the meanings of the two principal components
were interpreted. In the first principal component shownin Figure 4 (a), the dominance proportions of dry andfibrous were positive, whereas those of sweet and tenderwere negative during the first half period. Furthermore,salty was less dominant during the latter half of the task.
The findings indicate that the first principal component
exhibits the characteristics of ham with substantial dry and2.3 Bootstrap resampling of TDS tasks and TDS curve
By performing the TDS tasks described in Section 2.1,
we obtained 55 results (11 panels ൈ 5 replications) for
each product. The results of the TDS task included a setof continuous time functions. Each time function indicates
whether an attribute has been selected at any time.
In the TDS method, the TDS curves shown in Figure 2
were computed by combining the results of all panels. TheTDS curve shows the proportion of trials in which an
attribute was selected at any given time. To calculate this
proportion, the duration of each TDS task was normalized
from zero (start of the task) to one (end of the task).
Only a set of TDS curves is obtained from all panelists.
Hence, many curve sets are generated by bootstrap
resampling [10, 11] to apply statistical analysis to the TDS
curves. Following these methods [10, 11], TDS curves
were generated by randomly selecting 55 samples fromthe results of 55 tasks with replacement. For each type ofham, bootstrap resampling was performed 200 times togenerate 200 TDS curve sets. Hence, 1000 (200 ൈ 5) setsof TDS curves were calculated.
2.4 Principal component analysis of TDS curves
We performed principal component analysis on 10002

Fatty, salty,
non-sweet,
non-elastic

0.5
Ham B

Non-fatty,
non-salty,sweet, elastic

SecondPrincipalcomponent

Ham C
Ham A

0Ham EHam D0.5

0.4

00.4

First
principal component

Non-dry, non-fibrous, sweet,
and tender

0.8Dry, fibrous, non-sweet,
non-tender

Figure 3 Loci of 1,000 TDS curves on the principal component plane(a)

(b)

0.3
Dry

0.3

Fibrous

PC coefficients

PC coefficients

Fatty

0Sweet

Salty
0SweetTender
Salty

Elastic

0.3

0.3

0.1

0.5
Normalized time (-)

1

0.1

0.5
Normalized time (-)

1

Figure 4 Prominent coefficients of the (a) first and (b) second principal components
Unlike earlier studies that used non-temporal parameters,
our study utilized time-series information from TDS
methods. We demonstrate that the proposed method canseparate five types of hams on a two-dimensional
principal component plane. After testing it with other foodproducts, the general effect of the proposed method will
be validated.

fibrous textures, but little sweetener. Hams A and B
exhibit the aforementioned characteristics.
In the second principal component, shown in Figure 4(b), fatty was more dominant than the other attributes
throughout the entire task period. Salty was also
prominent in the first half. By contrast, sweet and elastic
were less dominant. These characteristics indicate that thesecond principal component was fatty and salty ham. Notethat being less elastic means that the ham was not resilient.
Hams B and C are located in the positive region of thesecond principal component.
Ham D was located approximately at the origin of theplane and was close to the average characteristic of thefive types of hams.

ACKNOWLEDGMENT
This study was supported in part by The Tojuro Iijima
Foundation for Food Science and Technology.
REFERENCES[1] International Organization for Standardization, ISO
13299: Sensory analysis—Methodology, 2016.
[2] S. Okamoto, Structural modeling of temporal
dominance responses using covariances ofcontemporary changes in subjective qualities,
International Journal of Affective Engineering, 20(3),pp. 127–130, 2021.
[3] K. Kantono, N. Hamid, D. Shepherd, Y. H. T. Lin, S.
Skiredj, B. T. Carr; Emotional and

5. CONCLUSION
The present study distinguished food products based ona time-series sensory analysis, namely, the TDS method.
We computed a principal component space, each of which
was established using the temporal changes in dominanceproportions of gustatory, olfactory, and textural attributes.

3electrophysiological measures correlate to flavour
perception in the presence of music, Physiology andBehavior, 199, pp. 154–164, 2019.
[4] T. Okada, S. Okamoto, and Y. Yamada; Affective
dynamics: Causality modeling of temporally evolving
perceptual and affective responses, IEEETransactions on Affective Computing, 13(2), pp.
628-639, 2019.
[5] K. Tachi and S. Okamoto; Dynamic state-spacemodeling with factorial memories in temporal
dominance of sensations, emotions and temporal
liking, IEEE Transactions on Affective Computing,2022. DOI: 10.1109/TAFFC.2022.3149133[6] G. Lecuelle, M. Visalli, H. Cardot, P. Schlich;
Modeling temporal dominance of sensations withsemi-Markov chains, Food Quality and Preference,
67, pp. 59–66, 2018.
[7] J. F. Rodrigues, V. R. de Souza, R. R. Lima, J. deDeus Souza Carneiro, C. A. Nunes, A. C. M.
Pinheiro, Temporal dominance of sensations (TDS)
panel behavior: A preliminary study with chocolate,Food Quality and Preference, 54, pp. 51–57, 2016.
[8] M. V. Galmarini , A-L. Loiseau, D. Debreyer, M.
Visalli, and P. Schlic, Use of multi-intake temporal
dominance of Sensations (TDS) to evaluate theinfluence of wine on cheese perception, Journal ofFood Science, 82(11), pp. 2669–2678, 2017.
[9] TDbear, https://www.comp.sd.tmu.ac.jp/hci/TDbear/[10] S. Okamoto, Bootstrap resampling of temporal
dominance of sensations curves to computeuncertainties, Foods, 10(10), article no. 2472, 2021.
[11] N. Shimaoka, S. Okamoto, Y. Akiyama, and Y.
Yamada, Linking temporal dominance of sensations
for primary-sensory and multi-sensory attributes
using canonical correlation analysis, Foods, 11(6),article no. 781, 2022.

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