Cross-Brand Machine Learning of Coffee's Temporal Liking from Temporal Dominance of Sensations Curves  
[PERSON] 1,2 and [PERSON] 1,3  
Department of Computer Science, Tokyo Metropolitan University, Hino 191-0065, Japan 1  
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote 2: email: {[PERSON]DDRESS]}  
Footnote 3: email: {[PERSON]DDRESS]}  
Footnote 4: email: {[PERSON]DDRESS]}  
Footnote 5: email: {[PERSON]DDRESS]}  
Footnote 6: email: {[PERSON]  
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@ed.tmu.ac.jp}  
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experiences, including causal modeling of interactions between sensory and emotional attributes [23] and stochastic modeling of sensory experience transitions [24; 25]. The TDS method is still in its developmental stages. As this method involves averaging multiple responses, it is currently not well-suited for addressing individual differences. However, approaches for analyzing differences among panels [26] and samples [27] are being developed. Furthermore, there are several variations of the TDS method [28], highlighting the need for protocol standardization. Similar to other sensory evaluation methods, collecting a large number of samples for TDS tasks is costly, limiting the volume of data available for most statistical and machine learning algorithms. Consequently, data-scientific applications of the TDS method require further research efforts. This challenge is expected to be addressed gradually through the implementation of resampling or data augmentation techniques [20; 29]. This study is part of a broader effort to apply machine learning algorithms to TDS data. Although the roots of the TL method are not actually new [30], the approach of combining the TDS and TL methods to analyze food tastes has drawn the interest of many researchers [15; 16; 17; 31; 32; 33; 34; 35; 36]. This is because this combination can serve as a framework for identifying the taste factors that determine temporal preferences. For example, [PERSON] et al. [15; 17] discussed the drivers of liking based on the average temporal liking scores when a specific taste attribute is being evaluated using the TDS method. We also consider the conjunctive use of the TDS and TL methods in this study. Attempts to replace temporal sensory evaluation with instrumented machines and computational methods have only recently begun. [PERSON] et al. [37] estimated the temporal changes in food texture from the time history of forces required for chewing food. [PERSON] and [PERSON] [38] proposed a method to predict the temporal changes in liking from TDS results using machine learning, demonstrating its feasibility with strawberries as an example. This study extends their work to cross-brand prediction. As shown in Figure 1, cross-brand or inter-brand prediction refers to the use of a predictive model trained on data from brands to estimate the temporal liking of another brand of the same food product. Demonstrating the effectiveness of machine learning predictions on untrained brands is a crucial step toward practical applications. This is the first research to investigate the feasibility of cross-brand estimation of temporal liking. The ultimate goal of this study is to replace part of the sensory evaluation conducted by human panels with machine-based methods, aiming to reduce costs and enhance the efficiency of food development and evaluation. Figure 1: Cross-brand prediction of a temporal liking curve from temporal dominance of sensations curves. Machine learning model trained on Brands A–C is used to predict the temporal liking of Brand D.  
In this study, we evaluated the cross-brand prediction performance of the method introduced by [PERSON] and [PERSON] [38], using instant coffee as an example. In their study, [PERSON] and [PERSON] [38] investigated the effectiveness of the machine learning method using TDS and TL task samples from the same strawberry brand. In contrast, this study involved TDS and TL tasks for four types of instant coffee from a single company.