Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves
The temporal dominance of sensations (TDS) method captures assessors’ real-time sensory experiences during food tasting, while the temporal liking (TL) method evaluates dynamic changes in food preferences or perceived deliciousness. These sensory evaluation tools are essential for understanding cons...
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MDPI AG
2025-01-01
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| author | Hiroharu Natsume Shogo Okamoto |
| author_facet | Hiroharu Natsume Shogo Okamoto |
| author_sort | Hiroharu Natsume |
| collection | DOAJ |
| description | The temporal dominance of sensations (TDS) method captures assessors’ real-time sensory experiences during food tasting, while the temporal liking (TL) method evaluates dynamic changes in food preferences or perceived deliciousness. These sensory evaluation tools are essential for understanding consumer preferences but are also resource-intensive processes in the food development cycle. In this study, we used reservoir computing, a machine learning technique well-suited for time-series data, to predict temporal changes in liking based on the temporal evolution of dominant sensations. While previous studies developed reservoir models for specific food brands, achieving cross-brand prediction—predicting the temporal liking of one brand using a model trained on other brands—is a critical step toward replacing human assessors. We applied this approach to coffee products, predicting temporal liking for a given brand from its TDS data using a model trained on three other brands. The average prediction error across all brands was approximately 10% of the maximum instantaneous liking scores, and the mean correlation coefficients between the observed and predicted temporal scores ranged from 0.79 to 0.85 across the four brands, demonstrating the model’s potential for cross-brand prediction. This approach offers a promising technique for reducing the costs of sensory evaluation and enhancing product development in the food industry. |
| format | Article |
| id | doaj-art-80e6eca59719480a800071d8ee7f2119 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-80e6eca59719480a800071d8ee7f21192025-01-24T13:21:27ZengMDPI AGApplied Sciences2076-34172025-01-0115294810.3390/app15020948Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations CurvesHiroharu Natsume0Shogo Okamoto1Department of Computer Science, Tokyo Metropolitan University, Hino 191-0065, JapanDepartment of Computer Science, Tokyo Metropolitan University, Hino 191-0065, JapanThe temporal dominance of sensations (TDS) method captures assessors’ real-time sensory experiences during food tasting, while the temporal liking (TL) method evaluates dynamic changes in food preferences or perceived deliciousness. These sensory evaluation tools are essential for understanding consumer preferences but are also resource-intensive processes in the food development cycle. In this study, we used reservoir computing, a machine learning technique well-suited for time-series data, to predict temporal changes in liking based on the temporal evolution of dominant sensations. While previous studies developed reservoir models for specific food brands, achieving cross-brand prediction—predicting the temporal liking of one brand using a model trained on other brands—is a critical step toward replacing human assessors. We applied this approach to coffee products, predicting temporal liking for a given brand from its TDS data using a model trained on three other brands. The average prediction error across all brands was approximately 10% of the maximum instantaneous liking scores, and the mean correlation coefficients between the observed and predicted temporal scores ranged from 0.79 to 0.85 across the four brands, demonstrating the model’s potential for cross-brand prediction. This approach offers a promising technique for reducing the costs of sensory evaluation and enhancing product development in the food industry.https://www.mdpi.com/2076-3417/15/2/948sensory evaluationreservoir computingecho state networkrecurrent neural network |
| spellingShingle | Hiroharu Natsume Shogo Okamoto Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves Applied Sciences sensory evaluation reservoir computing echo state network recurrent neural network |
| title | Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves |
| title_full | Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves |
| title_fullStr | Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves |
| title_full_unstemmed | Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves |
| title_short | Cross-Brand Machine Learning of Coffee’s Temporal Liking from Temporal Dominance of Sensations Curves |
| title_sort | cross brand machine learning of coffee s temporal liking from temporal dominance of sensations curves |
| topic | sensory evaluation reservoir computing echo state network recurrent neural network |
| url | https://www.mdpi.com/2076-3417/15/2/948 |
| work_keys_str_mv | AT hiroharunatsume crossbrandmachinelearningofcoffeestemporallikingfromtemporaldominanceofsensationscurves AT shogookamoto crossbrandmachinelearningofcoffeestemporallikingfromtemporaldominanceofsensationscurves |