Intelligent identification method of traditional Chinese medicine five tastes based on electronic tongue and sensory joint modeling
By combining electronic tongue and sensory modeling, the problems of detection correlation, system missingness, chemical heterogeneity and model interpretability in the identification of the five flavors of traditional Chinese medicine were solved. The systematic identification and intensity classification of the five flavors of traditional Chinese medicine were realized, the identification accuracy and model credibility were improved, and the pharmacopoeia standardization and regulatory requirements were met.
Patent Information
- Application Number
- CN202610456294.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-08
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies for identifying the five flavors of traditional Chinese medicine suffer from several problems, including insufficient correlation between detection methods and taste perception, lack of a five-flavor identification system, difficulty in adapting modeling methods to chemical heterogeneity, lack of taste intensity grading methods, insufficient model interpretability, and the risk of data leakage during model validation.
We employ a joint modeling approach combining electronic tongue and sensory input. Through dual-channel data acquisition, feature fusion, unsupervised exploratory analysis, heterogeneous stacked ensemble learning, and SHAP interpretability analysis, we construct a model for identifying the five flavors and intensity grading of traditional Chinese medicine. This model is then used to perform correlation mapping in conjunction with traditional Chinese medicine theories of sexuality.
It has achieved systematic and objective identification and intensity classification of the five flavors of Chinese herbal medicines, improved the identification accuracy of a few categories, provided a highly interpretable model, ensured the generalization ability and credibility of the model, and met the requirements of pharmacopoeia standardization and regulatory approval.
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Abstract
Citation Information
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