Abstract: Abstract: Rice is an important food ration of Chinese people, which contains a great number of starch, protein, fat and some nutrient elements. However, rice adulteration is becoming one of the most urgent problems and it needs to be solved as soon as possible in Chinese rice market. Therefore, the purpose of this study was to develop a rapid, precise and nondestructive method to detect the rice adulteration. In this paper, some expensive rice with high quality (Chang-Li-Xiang) and some cheap rice with relatively low quality (Li-Shui) were purchased from the local Wal-Mart in Zhenjiang province, China. Then they were mixed together in five different proportions (0:4, 1:3, 2:2, 3:1 and 4:0) by using electronic scale and the sample of rice adulteration were obtained. The visible and near infrared (VIS-NIR) hyperpectral imaging system with the spectral range of 390-1050 nm was used to capture the hyperspectral images of 200 rice samples. ENVI software was adopted to determine the region of interest (ROI) in the hyperspectral image and extract the hyperspectral data by averaging the reflectance from all the pixels in the ROI images. Then the discriminative model for rice adulteration was established by using support vector machine (SVM) and the extracted hyperspectral data in the full spectral range. The performance of the SVM model was evaluated by using the indexes of cross validation accuracy and prediction accuracy. Finally, the cross validation accuracy was 93% and the prediction accuracy was 98% in the full-spectral-SVM. As there were a large number of noise and redundant information in the raw hyperspectral images and hyperspectral data, some data processing methods should be used to remove the noise, accelerate the processing efficiency and improve the performance of the models. In this paper, the traditional principal component analysis (PCA) method was respectively used to process the hyperspectral images and hyperspectral data from the two aspects of feature selection and feature extraction. For the aspect of feature selection, a total of six characteristic wavelengths (531.1, 702.7, 714.3, 724.7, 888.2 and 930.6 nm) were picked up according to the weight coefficient distribution curve of the first four principal component images under the full wavelengths. For the aspect of feature extraction, the optimal number of principal component (PCs) was determined as 9 by using the leave-one-out cross-validation (LOOCV). Finally, the two kinds of simplified SVM models were respectively developed by using the input data at the six characteristic wavelengths and at the optimal PCs. The experiment results showed that the cross validation and prediction accuracy in the model based on characteristic wavelengths were 95% and 96%, the cross validation and prediction accuracy in the model based on optimal PCs were 94% and 98%. It indicated that the two kinds of simplified models all achieved the promising results and they all had the comparable discriminant power for rice adulteration when compared with the full-spectral-SVM. The results demonstrated that it is feasible to use hyperspectral imaging technology for the detection of the problem of rice adulteration.