Rapid identification of different processing degrees of Eucommiae cortex based on machine learning and explainable artificial intelligence.

This study integrated machine learning and explainable artificial intelligence (XAI) to classify four processing degrees of Eucommiae cortex (EC): raw, under-processed, moderately processed and over-processed. Powder characteristics (L⁎, a⁎, b⁎), and decoction piece characteristics (R, G, B, GRAY, and the percentage of breaking elongation (PBE)) of EC were collected via spectrophotometer, high-resolution camera, and material testing machine, respectively. Among the evaluated models, XGBoost demonstrated superior performance, achieving perfect training accuracy (100%) and test accuracy (94.74%). SHAP explanation identified PBE, a⁎ and h as key features. Furthermore, models based on decoction pieces characteristics outperformed powder features. The integrated Analytic Hierarchy Process (AHP)-Entropy Weight Method (EWM) analytical approach was acknowledged the relationship of appearance and components. This study successfully established an intelligent, rapid, and accurate method for identifying the processing degrees of EC decoction pieces, offering a valuable reference for at-line sampling quality control in industrial production.