Hulled Rice or husk? Synchrotron radiation XRF and deep learning approach for the determination of the geographical...

Accurate authentication of rice geographical origin is crucial for food safety and fraud prevention. Synchrotron radiation X-ray fluorescence (SR-XRF) spectroscopy was combined with deep learning to classify hulled rice (n = 903) and rice husks (n = 824) from 16 provinces in China. Distinct elemental fingerprints were observed, and PCA/t-SNE visualization confirmed clustering by origin. However, PCA-based discrimination was limited in resolving provinces with overlapping profiles, necessitating advanced nonlinear approaches. Three deep learning models-1D-CNN, 2D-VGG16, and 2D-AlexNet-were trained on SR-XRF spectra. 2D-AlexNet achieved the best results, with accuracies of 98.02% for hulled rice and 99.07% for husks, showing strong robustness across provinces. Husk-based classification outperformed hulled rice, highlighting rice husks as a reliable sample matrix. The SR-XRF-deep learning framework provides a rapid, non-destructive, scalable tool for rice traceability, surpassing conventional chemometric or isotope-based methods.