Explainable AI in Near‐Infrared Spectroscopy: A Case Study of Rice Protein Content

Accurate determination of rice protein is essential for quality control. This study evaluated machine learning models (partial least squares regression, PLSR; support vector machine, SVM) combined with feature selection algorithms (random frog, RF; competitive adaptive reweighted sampling, CARS; Monte‐Carlo uninformative wavelength elimination, MCUVE) and explainable artificial intelligence method (SHapley Additive exPlanations, SHAP) analysis to predict protein content. Results indicated that CARS‐PLSR achieved the highest accuracy (RMSEP = 0.266, R 2 P = 0.976, residual prediction deviation [RPD] = 6.612). Statistical analysis ( F ‐test and t ‐test) verified the model's reliability. Furthermore, SHAP analysis revealed that wavelengths at 1218, 1688, and 1209 nm made the highest contributions, corresponding to N–H and C–H vibrations. This study not only provides a rapid detection method but also elucidates the chemical basis of the model's high predictive performance. Practical Applications This study provides a high‐speed, non‐destructive method for accurately measuring rice protein content, allowing food processors to monitor grain quality in real‐time. By using “explainable” AI to reveal the specific chemical markers driving the results, this technology offers a transparent and reliable alternative to slow, expensive laboratory testing.