BACKGROUND: Rice adulteration in the food industry that infringes on the interests of consumers is considered very serious. To realize the rapid and precise quantitation of adulterated rice, a visible near infrared (VNIR) hyperspectral imaging system (380-1000 nm) was developed in the present study. A Savitsky-Golay first derivative (SG1) transform was utilized to eliminate the constant spectral baseline offset. Then, the adulterated levels of rice samples were quantified by partial least squares regression (PLSR). RESULTS:A SG1-PLSR model based on full-wavelength was attained with a coefficient of determination of prediction set (R P ) of 0.9909, root-mean-square error of prediction set (RMSE P ) of 0.0447 g kg −1 and residual predictive deviation (RPD P ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (B W ) and a simplified model (PLSR-15) was established with R P of 0.9769, RMSE P of 0.0708 g kg −1 and RPD P of 3.49. Finally, two visualization maps produced by applying the optimal models (SG1-PLSR and PLSR-15) were used to visualize the adulterated levels of rice.CONCLUSION: These results demonstrate that VNIR hyperspectral imaging system is an effective tool for rapidly quantifying and visualizing the adulterated levels of rice.

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The adulterated Wuchang rice were detected using hyperspectral imaging system with the optimal spectral pre-processing methods.