ISASE 2024

Prediction of Dynamic Preferenceby Using Temporal Dominance of Sensations DataHiroharu NATSUME*, Shogo OKAMOTO** Tokyo Metropolitan University, 6-6, Asahigaoka, Hino, Tokyo 191-0065, Japan

Abstract: Temporal dominance of sensations (TDS) method is a method to record multiple sensory changes over time during anexperience such as eating food. We recorded sensory changes and preference changes while eating strawberries by using TDS method.
Dynamic sensory and preference changes were trained on a reservoir computing network model, which estimated preferences withhigh accuracy for untrained sensory data. Reservoir computing has attracted attention as a fast machine learning method suitable fortime-series pattern recognition, and its application to the TDS method for handling multidimensional time-series data was demonstrated.
Keywords: Sensory evaluation, Reservoir computing, Strawberry

1. INTRODUCTION
2.1 TDS tasks
A graphical user interface on a computer is used in TDS
method experiments. Attribute words, which areadjectives that express sensations, are displayed as buttons
on the screen. The experiment participant (panel) starts thetask the moment he/she puts the food in his/her mouths.
The panel presses the button for the attribute word that isclosest to the current feeling while tasting the food.
Multiple buttons cannot be selected at the same time. Thepanel changes the button selection each time the dominantsensation changes. There can be buttons that are neverselected. When the food is swallowed, the panel ends thetask.

Temporal dominance of sensations (TDS) method is amethod to record multiple changes in sensory such liketaste. [1]. This method is actively used in the field of foodscience [2, 3] and is expected to spread to other fields inthe future.
TDS method records multiple sensory changes that are
multi-dimensional time-series data. Temporal likings,
which are changes in pleasant feeling while eating food,are also recorded in conjunction with the TDS method [4,5]. In sensory evaluation, temporal liking is an importantfactor as well as taste evaluation because it leads to humansatisfaction.
Recurrent neural networks (RNN) are used in patternrecognition of time-series data. Reservoir computing isone of the machine learning frameworks that use RNN [6].
In this method, the model is trained by fixing the weightsof the recurrent coupling layer of RNN and adjusting only
the readout layer, which makes it faster than conventional
RNN. There is no attempt to use reservoir computing for
TDS data.
The purpose of this study is to model the effect ofsensory changes during the experience of a product onchanges in preference for that product. We used the Echostate network (ESN), a typical model of reservoir
computing, to estimate product preferences from sensory
TDS data.
2. TEMPORAL DOMINANCE OF SENSATIONSMETHOD

Figure 1 Graphical user interface used for the task of
Temporal Dominance of Sensations method. Adaptedfrom [7].

1In this study, we record temporal liking by using TDS
method. The panel selects a button with a number from 1
to 9 written on it and answers on a 9-point scale how muchhe/she likes the product.

the assessors into two groups. From one group, we
produced 100 sets of TDS curves and the curve of
temporal liking following [8]. These curves were used fortraining the ESN model. From the other group of assessors,
we produced 25 sets of curves, then they were used as test
dataset.

2.2 TDS curves
The TDS curves are used to visualize the experimental
results of the TDS method. A TDS curve exists for each
attribute word, which shows the changes in the dominanceproportion, i.e., the proportion the attribute word selectedacross trials. The time axis is normalized between 0 (whenthe task starts) and 1 (when the task ends).
The Temporal liking curve differs from the sensory
curve in that it shows the evolution over time of theaverage liking across trials.

4. TDS METHOD FOR STRAWBERRIES
We followed the protocol of Shimaoka et al [9] andrecorded strawberry eating experiences using the TDS
method. 31 university students in their 20s performed thesensory TDS task and temporal liking task two to threetimes each, yielding 92 sensory and 93 liking data. Thetasks of sensory and temporal liking were conducted
alternately. For example, one panel conducted a task ofsensory followed by the task of liking. This process wasconducted three times for him/her. Referring to [9],attribute words used in evaluation of strawberries arearomatic, green, light, sweet, fruity, sour, watery.

3. PREDICTION OF TEMPORAL LIKING FROM
TIME-EVOLVING SENSATIONS VIA RESERVOIRCOMPUTING
3.1 Echo State Network
We use reservoir computing [6], a type of RNN, toestimate dynamic changes in a panel's preference for aproduct from TDS curves. In this study, we use the Echo
State Network (ESN), a typical neural network model forreservoir computing. The ESN model consists of threeunits: an input layer, a reservoir, and a readout. Thereservoir is a recurrent neural network that is randomlywired. Reservoirs can store inputs, making them suitable
for learning contextual time series data.

5. RESULTS
By using the trained ESN model, temporal liking wasestimated from the TDS data on 25 validation datasets.
(a)

3.2 ESN model used for predicting temporal liking
We constructed an ESN model for learning TDS curves.
As a preprocessing step, the continuous TDS curve wasdiscretized into 1000 intervals, yielding 1000 vectors withthe same dimensionality as the number of attribute words.
The dimension of the ESN input layer is set equal to thenumber of attribute words, and the readout dimension wasset to 1 in order to output temporal liking values. When
1000 vectors are input to the ESN model in time-seriesorder, the model outputs a time-series of liking.
In this study, the number of neurons in the ESN model
was set to 200.
Typical ESN parameters include leakage rate, spectral
radius, and input scaling. We set them to be 0.00467,1.83838, and 3.27857, respectively. A linear learner withlow computational cost was used to adjust the readoutlayer.
We used a bootstrap resampling method for TDS [8] toprepare for the training and test dataset. We first split allPreference ( -)(b)

Figure 2 TDS curve (a) and temporal liking (b) with thehighest coefficient of determination (𝑅2 = 0.9949).

2The resulting temporal liking curve with the largest
coefficient of determination (𝑅 2 = 0.9949) and the TDS
curve as input are shown in Figure 2. Figure 3 shows theTDS curve and temporal liking for the case with thelowest coefficient of determination (𝑅2 = 0.8848).

ETHICAL STATEMENT
The protocol of this study was approved by Institutional
Review Board, Hino Campus, Tokyo Metropolitan
University (H23-34).

6. CONCLUSION
Reservoir computing has never learned TDS data before.
We experimentally collected TDS data and temporal
likings of strawberry eating experiences and built an ESN
model to estimate preferences from the sensory data. The
model estimated the corresponding preference fromuntrained data with an accuracy of 𝑅2 = 0.99 − 0.88 .
The model should be evaluated statistically in the future.
It is hoped that reservoir computing will make it possible
to estimate the results of temporal liking tasks from TDStask results alone.

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ACKNOWLEDGMENTS
This study is in part supported by Lotte Foundation (Thisstudy was in part supported by Lotte Foundation (2023-B1).
(a)Preference ( -)(b)

Figure 3 TDS curve (a) and temporal liking (b) with thelowest coefficient of determination (𝑅2 = 0.8848).

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