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Rapid quantitative analysis of adulterated rice with partial least squares regression using hyperspectral imaging system
Author(s) -
Guo Lianbo,
Yu Yunxin,
Yu Hanyue,
Tang Yun,
Li Jun,
Du Yu,
Chu Yanwu,
Ma Shixiang,
Ma Yuyang,
Zeng Xiaoyan
Publication year - 2019
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.9824
Subject(s) - partial least squares regression , vnir , hyperspectral imaging , mathematics , mean squared error , residual , food science , statistics , chemistry , artificial intelligence , computer science , algorithm
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. © 2019 Society of Chemical Industry