Accuracy and stability improvement in detecting Wuchang rice adulteration by piece-wise multiplicative scatter correction in the hyperspectral imaging system

Abstract

The adulteration of rice in the food industry is a very serious problem nowadays. To realize the rapid and stable identification of adulterated Wuchang rice, a hyperspectral imaging system (380–1000 nm) has been introduced in this study. Piece-wise multiplicative scatter correction (PMSC) was first used to correct the non-linear additive and multiplicative scatter effects. Then, the adulterated rice samples were identified via support vector machines (SVM). The PMSC-SVM model was attained over the whole spectral range, with the correct classification rate (CCR) increased from 95.47% to 99.20%, the kappa coefficient increased from 0.95 to 0.99, and the prediction CV (coefficient of variation) decreased from 3.04% to 1.56%. Furthermore, a simplified PMSC-SVM model was established, where 13 principal components were selected using 5-fold cross-validation. The CCR was increased from 95.40% to 99.08%, the kappa coefficient was increased from 0.94 to 0.99, and the prediction CV was decreased from 3.02% to 1.72%. The results demonstrated that the accuracy and stability for identifying adulterated rice has been improved by PMSC in the hyperspectral imaging system.