Geographical Origin Identification of Rhizoma Atractylodis macrocephalae Using Hyperspectral Imaging Combined with Broad Learning System and SHapley Additive exPlanations

Rhizoma Atractylodis macrocephalae (RAM) is a renowned food–medicine homologous herb in China, the quality and efficacy of which are inherently linked to its geographical origin. However, traditional origin identification methods for RAM are time-consuming, laborious, and destructive. This study introduces an innovative framework integrating hyperspectral imaging (HSI), broad learning system (BLS), and SHapley Additive exPlanations (SHAP) for RAM origin identification. RAM samples were collected from three origins, 100 samples from per origin, and imaged using a visible and short-wave near-infrared HSI system. BLS was used to build identification models with full and important wavelengths, and compared against seven traditional algorithms, including K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), gradient boosting decision tree, (GBDT), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Additionally, SHAP was used to enhance interpretability and identify important wavelengths highly correlated with RAM origin. Results showed that the full-wavelength BLS model achieved a test accuracy of 95.56%, which outperformed other models including KNN (77.78%), RF (85.56%), GBDT (88.89%), AdaBoost (90.00%), BPNN (91.11%), XGBoost (92.22%), and SVM (94.44%). SHAP identified important wavelengths similar to traditional methods (competitive adaptive reweighted sampling and successive projections algorithm), and the BLS model using SHAP-selected top 25 wavelengths achieved 94.44% accuracy with minimal performance loss. This study not only provides a rapid and accurate approach for RAM origin identification but also establishes a promising data-driven paradigm for non-destructive geographical origin traceability of other traditional Chinese medicines.