The widespread issue of adulterating premium rice with cheaper or broken grains significantly diminishes both its nutritional quality and economic value. Traditional methods, such as visual inspection and near-infrared spectroscopy, often prove inadequate when classifying morphologically similar cultivars or analyzing large, bulk quantities. To overcome these limitations, this study introduces and validates a completely non-destructive acoustic methodology for the classification of five major commercial rice cultivars, Jasmine, Basmati, Hashemi, Lenjan, and Broken Lenjan and their intentional mixtures with a lower-grade rice, Anbarbo. We analyzed 1000 bulk samples across four adulteration levels (100, 85:15, 70:30, and 50:50) by measuring sound absorption coefficients (350–1895 Hz) using a four-microphone impedance tube. The raw spectra, following minimal preprocessing, were classified using a customized DeepSpectra convolutional neural network. The DeepSpectra model achieved a robust 84.0% overall accuracy and an F1-score of 0.81 on independent test data, markedly surpassing the performance of both PLS-DA (61%) and a shallow ANN (69%) on pure samples. Interestingly, mid-ratio mixtures (70:30 and 50:50) yielded the highest classification accuracies of 87–89%, which we attribute to the emergence of distinctive spectral signatures. This rapid (under 30 s per sample), low-cost technique is ideal for bulk analysis, providing substantial practical benefits for industrial quality control and verifying the authenticity of granular commodities like rice.
