An integrated machine learning framework for TCM five-flavor classification based on E-tongue profiling and SHAP analysis
Abstract
Background: The "five-flavor" (wu wei) classification is a core organizing principle in Traditional Chinese Medicine (TCM) pharmacology and quality control, yet its assessment still relies on subjective organoleptic evaluation, limiting standardization and reproducibility.
Methods: Eighty-four processed herbal slices were analyzed using an electronic tongue to obtain multichannel sensor response profiles. Based on these data, we developed a dual-strategy ensemble learning framework in which a heterogeneous stacking model was trained for five-flavor identification while a voting ensemble was established in parallel for intensity grading. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP) to quantify feature contributions and to relate prediction outcomes to specific electrochemical response patterns.
Results: On the independent test set, the stacking ensemble showed moderate and flavor-dependent classification performance, with a macro-AUC of 0.876 and a balanced accuracy of 0.629. Discriminative performance was higher for Bitter and Sweet than for Sour, Salty, and Pungent. Full-stack SHAP analysis further identified P_13 and P_8 as the most influential features overall, with P_13 contributing mainly to the discrimination of bitter and pungent attributes, whereas P_8 was more strongly associated with sour, salty, and sweet characterization.
Conclusions: This study demonstrates a data-driven workflow for digitizing TCM five-flavor assessment.…
Read the paper
Related studies
- Nano Biosensors: Properties, applications and electrochemical techniques
- Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics
- Toward a Harmonization for Using in situ Nutrient Sensors in the Marine Environment
- Electrochemical determination of rutin by using NiFe2O4 nanoparticles-loaded reduced graphene oxide
- Application of Polyoxometalate-based composites for sensor systems: A review
- Sensitive Electrochemical and Thermal Detection of Human Noroviruses Using Molecularly Imprinted Polymer Nanoparticles Generated against a Viral Target
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.