A hyperspectral imaging technology combined with the principal component analysis (PCA) and the minimum noise fraction (MNF) methods were developed for the detection of common defects in Lingwu long jujubes, and investigated the influence of the background to recognition of defects. Firstly, the hyperspectral images of jujube samples (insect hole, crack and intact jujubes) were acquired. Secondly, the PCA and MNF methods were used to reduce dimensionality of hyperspectral images and to separate the noise from signals effectively. The PC1 and M1 images of insect hole and intact jujube, PC2 and M2 images of crack jujube were selected to distinguish different type of jujubes. By the PCA method, the classification rates of three kinds of jujubes all were 100%. And by the MNF method, the classification rates of insect hole jujubes, crack jujubes and intact jujubes were 69.2%, 56.8%, 100%, respectively. Then, the masked original hyperspectral images were to remove the effect of background and analyzed by the PCA and MNF method again. The classification rates by the PCA method were all 100%, and the classification rates by the MNF method were 73.1%, 65.9%, 100%, respectively. The results showed that the hyperspectral imaging technology combined with PCA and MNF methods were feasible. The influence of the background by the MNF method to defect recognition was slight and the impact to defect recognition by the PCA method gained the advantage over the MNF method. The recognition rate of the MNF method combined with background mask was better than that of no background mask, and to provide the theory basis for the common defects of online detection in future.

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