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10%。A non-destructive detection method based on near infrared reflectance (NIR) spectroscopy and chemometrics was put forward for discriminating varieties and detecting soluble solids content (SSC) of fresh jujube. A FieldSpec 3 spectroradiometer was used for collecting 30 sample spectra data of the three kinds of jujube separately. Then principal component analysis was used to process the spectral data after pretreatment. Six principal components (PCs) were selected based on accumulative reliabilities, and these selected PCs would be taken as the inputs of the three-layer back-propagation artificial neural network (BP—ANN). A total of 90 jujube samples were divided into calibration set and validation set randomly, the calibration set had 75 samples with 25 samples of each variety and the validation set had 15 samples with 5 samples of each variety. The BP—ANN was trained using samples in calibration set. The optimal three-layer BP—ANN model with 6 nodes in input layer, 10 nodes in hidden layer, and 2 nodes in output layer would be obtained. Then this model was used to predict the sample in the validation set. The result show that a 100% recognition ration was achieved with the threshold predictive error ±0.17, the bias between predictive value and standard value was lower than 10%.