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To detect bruised kiwifruits from intact kiwifruit early and effectively, near infrared diffused reflectance spectroscopy technology combined with Fisher discriminant function, BP neural network and least square support vector machine(LSSVM) were applied to discriminate collided kiwifruit, pressed kiwifruit and intact kiwifruit after storage of 1d, respectively. Effectiveness of the discriminant model using full spectrum(FS), feature variables based on principal component analysis(PCA) and characteristic wavelength by successive projection algorithm(SPA) was compared and evaluated. The results showed that SPA gave the best advantage compared with methods of FS and PCA. Three models all had an acceptable accuracy, especially LSSVM model had the optimal recognition performance. SPA-LSSVM had an accuracy rate of 100%, 95% and 100% for identifying collided samples, pressed fruits and intact ones respectively, and the discriminant accuracy rate for total samples was 98.2%. 