Research on data-driven rapid nondestructive quality evaluation method, Calculus Bovis as an example.

To address the low accuracy of traditional methods for animal-derived products due to spatial heterogeneity and limited portable tools resolution. This study built a data-driven rapid non-destructive framework integrating portable near infrared, multi-location spectral fusion, and machine learning. It applied low-level (raw spectral stitching) and mid-level (feature stitching) fusion for Calculus bovis heterogeneity, combined with variable selection. Qualitatively, the accuracy of the optimized linear model achieved 96.70%. Quantitatively, the mid-level model demonstrated superior performance compared to the other models, achieving the R2 value of 0.9450 and the RPD value of 2.89. This framework meets the demands of analytical chemistry for on-site efficacy and qualitative/quantitative precision and provides a transferable paradigm for complex natural products.