Rapid identification of adulterated rice using fusion of near-infrared spectroscopy and machine vision data: the combination of feature optimization and nonlinear modeling

Rice is susceptible to mold and mildew during storage. Metabolites such as aflatoxin produced during mildew will do great harm to consumers. To meet the need for rapid detection of normal rice adulterated with moldy rice, a rapid identification method of adulterated rice was established based on data fusion of near-infrared spectroscopy and machine vision. Using competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), and least angle regression (LARS) for spectral and image feature extraction, combined with support vector classification (SVC), random forest (RF), and gradient boosting tree (GBT) nonlinear discriminant models, and use Bayesian search to optimize modeling parameters. The results show that the GBT fusion data model established by LARS optimization of spectral and image feature variables has the highest discrimination accuracy, with recognition accuracy rates of 100.00% and 98.11% for its training and testing sets, respectively. The discrimination performance is significantly improved compared to single near-infrared spectroscopy and machine vision. The results indicate that rapid identification of adulterated rice based on near-infrared spectroscopy and machine vision data fusion technology is feasible, providing theoretical support for the development of online identification equipment for adulterated rice.