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Updated: Jun 6, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

An integrated machine learning framework for TCM five-flavor classification based on E-tongue profiling and SHAP

Ziang Li^1,2,3,4, Xianglong Meng^5,6,7,8, Lin Yang^1,2,3,4

¹College of Chinese Materia Medica and Food Engineering, Shanxi University of Chinese Medicine, Jinzhong, 030619, China.

Chinese Medicine

|June 5, 2026

Summary

This study developed an electronic tongue method for objective Traditional Chinese Medicine (TCM) five-flavor assessment, improving standardization. The data-driven workflow shows promise for digitizing TCM quality control.

Keywords:

Electronic tongues Explainable artificial intelligence Five flavors Heterogeneous stacking ensemble Machine learning SHAP analysis

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

µTongue: A Microfluidics-Based Functional Imaging Platform for the Tongue In Vivo

Published on: April 22, 2021

Area of Science:

Pharmacology
Analytical Chemistry
Computational Biology

Background:

Traditional Chinese Medicine (TCM) relies on a "five-flavor" (wu wei) system for pharmacology and quality control.
Current organoleptic evaluation methods lack standardization and reproducibility.

Purpose of the Study:

To develop an objective, data-driven method for assessing the five flavors of TCM using an electronic tongue.
To establish a computational framework for flavor identification and intensity grading.

Main Methods:

An electronic tongue analyzed 84 processed herbal slices, generating multichannel sensor response profiles.
A dual-strategy ensemble learning framework, including stacking and voting models, was developed.
SHapley Additive exPlanations (SHAP) were used for model interpretability and feature analysis.

Main Results:

The stacking ensemble achieved moderate flavor classification performance (macro-AUC 0.876, balanced accuracy 0.629).
Bitter and Sweet flavors were classified more accurately than Sour, Salty, and Pungent.
SHAP analysis identified key sensor features (P_13, P_8) associated with specific flavor profiles.

Conclusions:

A data-driven workflow for digitizing TCM five-flavor assessment was demonstrated.
Further validation with larger sample sizes and chemical corroboration is necessary for practical application.