Near Infrared Spectroscopy Combining with Chemometrics for Qualitative Identification of Cadmium-Polluted Rice

Near-infrared (NIR) diffuse reflectance spectroscopy and chemometrics method were used to discriminate cadmium. polluted rice. The samples set contained 120 spectra of qualified (n=49) and excessive (n=71) was collected and scanned. After optimization, a combination (smoothing coupled with first derivative and mean centering) was utilized as a spectral pretreatment method. Competitive adaptive reweighed sampling (CARS) was adapted to selected 45 key variables, and each band of the variables was assigned. Five modeling methods including partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), K-nearest neighbor (KNN), soft independent modeling class analog (SIMCA) and principal component analysis. discriminant analysis (PCA-DA) were used and compared. PCA-DA was finally selected as the optimal qualitative model. The accuracy rate of training set and testing set for PCA-DA method was 98.8% and 91.7%, respectively. The results showed that NIR spectroscopy could be used as a rapid, non. destructive and convenient analytical method for primary screening and detecting cadmium. polluted rice.