There were 1 000 single-grain rice samples collected from 10 origins and 4 varieties in Northeast and non-Northeast China.Near-infrared hyperspectral images were acquired within the wavelength range of 950 nm to 1 700 nm.The region of interest was selected according to the outline of single rice from the images to calculate average spectra.Firstly,principal component analysis was used to extract the first and second principal components with a cumulative contribution rate greater than 99%.According to the maximum value of the weight coefficient in the loading matrix,the characteristic wavelengths of the first and second principal components of 1 396.67 nm and 1 467.38 nm are screened respectively.The principal component analysis was performed on the two sets of characteristic wavelength images,and the first three-dimensional principal components were selected respectively,and a total of 2×3 sets of training sample sets can be obtained.Finally,there were 6 models established to discriminate Northeast/ Non-Northeast rice.Among them,the best performing model was built based on the 1 467.38 nm third principal component image,and its recognition accuracy can reach 99.5%.