数字农科院2.0

Neuro-sensory evaluation of citrus flavors: A hierarchical spatial fusion approach for emotion-driven food innovation

文献类型： 外文期刊

作者： Zhao, Qian;Yang, Peilin;Liang, Yushen;Xu, Zhenzhen;Chen, Jianle;Chen, Shiguo;Ye, Xingqian;Cheng, Huan

作者机构：

关键词： Emotion-driven food;Electroencephalography;Transformer;Olfactory perception;Citrus flavor

期刊名称： FOOD RESEARCH INTERNATIONAL

ISSN： 0963-9969

年卷期： 2025 年 226 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Emotion-driven food is gaining increasingly attention in the food industry; yet conventional sensory evaluations remain constrained by subjective self-reports. To enable a more comprehensive understanding of flavor perception, this study incorporated both emotional assessment and implicit neurophysiological measurement in the evaluation of different citrus flavors. A novel hierarchical spatial fusion strategy (HSFS) was proposed to integrate electroencephalography (EEG) signals with self-reported affective responses based on the customized Pleasure-Arousal-Dominance (PAD) scales. Results revealed that positive emotions were predominant subconscious responses to citrus flavor, and significant correlations were observed between emotional states and flavor acceptability scores. While the emotional labels across individuals appeared consistent, their underlying EEG activation patterns exhibit considerable variability. Under the optimal parameters (learning rate = 0.0001, batch = 64, hidden size = 640, and four attention heads and layers), the proposed HSFS-Transformer effectively integrated the comprehensive features from both global and local attention modules. This model achieved strong performance in binary and four-class emotion classification, with accuracies of 88.84 % and 82.05 %, respectively. Additionally, it demonstrated robust results with F1-scores of 86.12 % (binary) and 81.72 % (multi-class), and Cohen's Kappa values of 72.26 % and 75.21 %, confirming the reliability and consistency of the predictions. Comparative experiments further validated the architecture, highlighting its strength in capturing the complex and sparse dynamics of EEG signals. This research advanced sensory analytics by bridging neurophysiological insights and consumer feedback, offering a promising tool for precision formulation of emotion-modulating functional foods and data-driven product innovation.

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