Advanced data-driven interpretable analysis for predicting resistant starch content in rice using NIR spectroscopy.

Resistant starch (RS) is a vital dietary component with notable health benefits, but tradition quantification methods are labor-intensive, costly, and unsuitable for large-scale applications. This study introduced an innovative data-driven framework integrating Near-Infrared (NIR) spectroscopy with Convolutional Neural Networks (CNN) and data augmentation to achieve rapid, cost-effective RS prediction. Achieving exceptional accuracy (Rp = 0.992), the CNN model outperformed traditional methods like Partial Least Squares Regression (PLSR) and Support Vector Machine Regression (SVMR). To overcome the "black-box" limitation of deep learning, SHapley Additive exPlanations (SHAP) were innovatively employed, pinpointing critical wavelengths (2000-2500 nm), significantly narrowing the spectral range while providing meaningful insights into the contribution of specific wavelengths to RS prediction. This optimized spectral enhanced data acquisition efficiency, reduces analytical costs, and simplifies operational complexity, establishing a practical and scalable solution for deploying NIR spectroscopy in food quality assessment and production-line applications.