Interpretable Spectral Evidence Learning from Vis/NIR Imaging for Non-Destructive Authentication of Herbal Medicines

Rapid and non-destructive authentication of herbal medicines is important for quality control and market supervision. This study established an interpretable spectral evidence learning framework for visible and near-infrared (Vis/NIR) imaging-based authentication of Codonopsis Radix (CR) and Aurantii Fructus (AF). Compact 31-band mean gray-value spectra were analyzed at ROI and sample levels. CR sample-level spectra were obtained by ROI-group averaging, whereas AF records were retained as individual sample spectra with image-group information used for leakage-controlled validation. Raw spectra, Savitzky-Golay smoothing, multiplicative scatter correction, and standard normal variate correction were compared with machine-learning and deep-learning classifiers. A fold-contained lightweight diffusion (LD) module was further introduced to provide class-conditioned spectral augmentation and denoising-error evidence. Under grouped cross-validation, the strongest non-LD Linear SVM models achieved accuracy/macro-F1 values of 0.9231/0.9238 for CR and 0.9025/0.9018 for AF. After LD augmentation, the best LD-augmented SVM models reached macro-F1 values of 0.9427 and 0.9197, respectively. Across all evaluated model-dataset combinations, LD increased the overall mean macro-F1 from 0.7302 to 0.8189. Model-aligned wavelength evidence and top-wavelength subset tests further showed that selected LED-band subsets retained useful discriminative information within the present imaging configuration. These results support the feasibility of compact Vis/NIR image-based authentication of herbal materials under grouped validation.