Display omitted Detection of common defects on jujube based on Vis-NIR and NIR hyperpespectral imaging technique. •Hyperspectral imaing (HSI) was used to classify different defects in jujube.•Spectra in Vis-NIR/NIR were extracted to identify different jujubes.•Different spectral parameters of SIMCA models were achieved.•Models based on SVM and SIMCA were developed.•It is feasible to detect defective jujubes by spectral information of HSI. A hyperspectral imaging technique was used for acquiring reflectance images to identify common defects (bruise, insect-infestetation and cracks) on jujube fruit. Hyperspectral images of jujubes were evaluated from the regions of interest through principal component analysis (PCA) to select five optimal wavelengths (420,521,636,670,679nm) from 300 samples in the spectral region of 400–1000nm and four important wavelength (1028,1118,1359,1466nm) in the region of 978–1586nm. Compared with support vector machine (SVM) models, the soft independent modeling of class analogy (SIMCA) models of intact, cracked, bruised, and insect-infested jujubes based on five wavelengths in NIR showed good performance with high classification rates of 96%, 96%, 93.9% and 95.6%, respectively. This research demonstrates the feasibility of implementing hyperspectral imaging for identifying common defects and enhancing the product quality and marketability.