Quality Detection of Winter Jujube from South Xinjiang Based on Polarized Hyperspectral Imaging
The hyperspectral data(900-1 750 nm)of non-polarization and at four polarization angles(0°,45°,90°,and 135°)were collected for winter jujube from South Xinjiang and red grapes(used for validation).The original spectra were processed by envelope removal.The competitive adaptive reweighted sampling algo-rithm was used for the dimension reduction of data,and the most effective wavelengths were selected.A partial least squares regression(PLSR)model was established with the reflectance data from one non-polarized and four polarized directions to predict the moisture content and soluble solid content(SSC)in winter jujube from South Xinjiang.Compared with non-polarized hyperspectral modeling,the models with data from spectral data with the polarization angles of 90°and 135°showed improved performance in predicting the moisture content and SSC of winter jujube,achieving correlation coefficients of 0.958 8 and 0.924 3,respectively,and remaining prediction deviations greater than 2.Similar results were obtained in the modeling for red grapes.The findings demonstrated that polarized hyperspectral imaging outperformed non-polarized hyperspectral imaging in model-ing.The modeling with the data from hyperspectral imaging at the polarization angles of 90°and 135°had the highest accuracy in predicting the moisture content and SSC of winter jujube.
polarizedpolarized hyperspectral imagingpartial least squares regression(PLSR)quality de-tectionwinter jujub
万方数据
维普
