An rapid nondestructive testing method for distinguishing rice producing areas based on Raman spectroscopy and support vector machine

Abstract

This study reports method for rapid and nondestructive identification of single-grain rice produced in neighboring areas. Four types of representative rice from different areas were selected as experimental samples. A total of 284 single-grain rice Raman spectra were acquired and spectral information in the 400–1700 cm−1 spectral area was extracted for analysis. First, the samples were divided into 190 calibration sets and 94 validation sets by the Kennard–Stone method, and the raw spectra were pretreated using smoothing and differential methods. Next, principal component analysis (PCA) and a successive projections algorithm (SPA) were used to extract the optimal principal components and effective wavelengths, which were used as the input variables for k-NearestNeighbor (KNN) and least-squares support vector machine (LS-SVM) algorithms. The PCA-KNN,SPA-KNN,PCA-LS-SVM and SPA-LS-SVM models were established based on information from the rice spectra. Finally, the model was applied to classify the validation set samples. The recognition accuracies of the PCA-KNN,SPA-KNN, PCA-LS-SVM and SPA-LS-SVM models for the validation set are 91.43 %,93.62 %,91.49 and 94.68 %, respectively. These results indicate that single-grain rice from different producing areas can be identified by using Raman spectroscopy combined with KNN and LS-SVM. This method can achieve rapid and completely non-destructive testing.