Near-Infrared Hyperspectral Imaging for Non-Destructive Detection of Old Rice in Freshly Milled Rice

Adulteration of freshly milled rice with rice from older sources is a fraudulent and illegal practice that exploits consumers. The purpose of this study was to develop a rapid and non-destructive technique that can detect this adulteration of milled rice, using near-infrared hyperspectral imaging (NIR-HSI) in the wavelength range of 935-1720 nm. Adulterated samples were prepared by adding old and freshly milled rice at different levels, scanning the mixed samples, and comparing the results with 100% freshly milled rice samples. All samples were divided into calibration and prediction sets for the development of classification and calibration models. Spectral pretreatment methods were tested to develop the optimum models. For qualitative prediction, the best results for differentiation between freshly milled rice and adulterated samples using partial least squares discriminant analysis (PLS-DA) yielded 96.15% accuracy, 92.86% sensitivity, and 100% specificity. For quantitative prediction, the best calibration model for determining the percentage of mixing with old rice using support vector machine regression (SVMR) yielded a coefficient of determination for prediction (R²_p) = 0.90, and root mean square errors of prediction (RMSEP) = 9.10%. These findings demonstrate the potential of NIR-HSI for both qualitative and quantitative analyses in detecting the adulteration of freshly milled rice with old rice. It can be used as a rapid, nondestructive technique for assessing the authenticity of milled rice.