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草莓味觉的时间主导性与时间喜好曲线之间的协同分析

Synergy Analysis Between the Temporal Dominance of Sensations and Temporal Liking Curves of Strawberries.

作者信息

Okamoto Shogo, Natsume Hiroharu, Watanabe Hiroki

机构信息

Department of Computer Science, Tokyo Metropolitan University, Tokyo 192-0397, Japan.

出版信息

Foods. 2025 Mar 14;14(6):992. doi: 10.3390/foods14060992.

Abstract

The Temporal Dominance of Sensations (TDS) method allows for the real-time tracking of changes in multiple sensory attributes, such as taste, aroma, and texture, during food tasting. Over the past decade, it has become an essential tool in sensory evaluation, offering novel insights into temporal sensory perception. When combined with the Temporal Liking (TL) method, TDS enables the investigation of how sensory changes influence instantaneous liking. Existing methods in time-series sensory evaluation have not simultaneously achieved the following two key objectives: (1) predicting TL curves from TDS curves and (2) identifying shared sensory-liking synergies across samples. In this study, we address this gap by applying supervised non-negative matrix factorization, which enables both precise prediction and interpretable synergy extraction. This novel approach has the potential to extend the applicability of TDS analysis to broader sensory evaluation contexts. We validated the method using the data for strawberries recorded in an earlier study. Our model, utilizing three latent synergy components accounting for 94% of the data variation, accurately predicted the TL curves from TDS curves with a median RMSE of 0.36 in cross-validation, approximately 1/16 of the maximum TL score. Moreover, these synergy components were highly interpretable, suggesting some key factors that explain individual variations in sensory perception. These findings highlight the effectiveness of synergy analysis in time-series sensory evaluation, leading to deeper understanding of the connections between temporal sensory and liking responses.

摘要

味觉主导性(TDS)方法能够实时追踪食品品尝过程中多种感官属性(如味道、香气和质地)的变化。在过去十年中,它已成为感官评价中的一项重要工具,为时间感官知觉提供了新颖的见解。当与时间喜好(TL)方法相结合时,TDS能够研究感官变化如何影响即时喜好。时间序列感官评价中的现有方法尚未同时实现以下两个关键目标:(1)从TDS曲线预测TL曲线;(2)识别不同样品间共享的感官-喜好协同效应。在本研究中,我们通过应用监督非负矩阵分解来填补这一空白,该方法能够实现精确预测和可解释的协同效应提取。这种新方法有可能将TDS分析的适用性扩展到更广泛的感官评价背景中。我们使用早期研究中记录的草莓数据对该方法进行了验证。我们的模型利用三个潜在协同成分,它们占数据变异的94%,在交叉验证中从TDS曲线准确预测了TL曲线,中位数均方根误差(RMSE)为0.36,约为最大TL分数的1/16。此外,这些协同成分具有高度可解释性,表明了一些解释感官知觉个体差异的关键因素。这些发现突出了协同效应分析在时间序列感官评价中的有效性,有助于更深入地理解时间感官与喜好反应之间的联系。

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