Lianbo Guo1, Yunxin Yu1, Hanyue Yu1, Yun Tang1, Jun Li2, Yu Du3, Yanwu Chu1, Shixiang Ma1, Yuyang Ma1, Xiaoyan Zeng1. 1. Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology, Wuhan, China. 2. School of Geography and Planning and Guangdong Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou, China. 3. College of Communication Engineering, Jilin University, Changchun, China.
Abstract
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 (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP 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.
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 (RP ) of 0.9909, root-mean-square error of prediction set (RMSEP ) of 0.0447 g kg-1 and residual predictive deviation (RPDP ) of 11.28. Furthermore, fifteen important wavelengths were selected based on the weighted regression coefficients (BW ) and a simplified model (PLSR-15) was established with RP of 0.9769, RMSEP of 0.0708 g kg-1 and RPDP 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.