A nondestructive and rapid method for in situ measurement of crude fat content in soybean grains.

Sample heterogeneity compromises near-infrared (NIR) accuracy for crude fat content (CFC) in soybeans, traditionally requiring destructive grinding. Existing methods lack real-time nondestructive alternatives. We hypothesized that Raman-NIR fusion via an enhanced machine learning framework could enable precise, non-destructive CFC quantification. An enhanced broad learning system (EBLS) integrating NIR and Raman spectra was developed. The EBLS achieved accuracy comparable to destructive methods (RSQ > 0.92), outperformed existing grain-level techniques, and reduced moisture interference versus standalone NIR. Statistical validation confirmed no significant differences from national standards (95 % confidence). This multimodal spectral fusion paradigm allows real-time CFC analysis during soybean transactions and adaptive industrial parameter optimization, addressing preprocessing-free measurement needs.