No abstract

To predict soluble solids content (SSC) of jujube fruits, a hyperspectral imaging technique has been used for acquiring reflectance images from 200 samples in the spectral regions of 900-1700nm. Hyperspectral images of jujubes were evaluated from the regions of interest using principal component analysis (PCA) with the goal of selecting five optimal wavelengths (1034, 1109，1231，1291 and 1461nm). Prediction model of SSC (Rp=0.9027, RMSEP=1.9845) were built based on BP neural network. This research has demonstrated the feasibility of implementing hyperspectral imaging technique for sorting jujube fruit for SSC to enhance the product quality and marketability.