TCM-USP: An Adaptive and Interpretable Framework for Rapid, Traceable Preliminary Quantification of Active Constituents in Traditional Chinese Medicines Using Biomimetic Sensor Data

Abstract

Biomimetic sensors offer a rapid route for estimating active constituent contents in Chinese medicinal materials, but their practical use at procurement sites and production workshops is constrained by small and skewed datasets, repeated task-specific model configuration, and the limited operational traceability of many nonlinear models. This study proposes the Traditional Chinese Medicine Unified Sensor-based Prediction (TCM-USP) framework for traceable preliminary screening rather than confirmatory laboratory quantification. The same modeling workflow was applied across different medicinal material–sensor combinations and integrates controlled symbolic feature construction with density-aware robust partial least squares modeling. The framework was evaluated on six prediction tasks involving five medicinal materials and three sensor types. TCM-USP achieved higher R2 values than raw-feature PLS in all six tasks and achieved the highest R2 among the evaluated models in four tasks. Although SVR or GPR achieved higher R2 values in the remaining two tasks, TCM-USP generated compact prediction formulas directly expressed in terms of the original sensor readings, enabling independent calculation, audit, and rapid batch-level decision support. These results support the feasibility of TCM-USP for traceable preliminary screening across the investigated small-sample and low-dimensional sensor tasks, while pharmacopoeial methods remain necessary for confirmatory quantification.