| Citation: | LI Bin, WAN Xia, LIU Ai-lun, ZOU Ji-ping, LU Ying-jun, YAO Chi, LIU Yan-de. Optimal position for suger content detection of Yongquan honey oranges based on hyperspectral imaging technology[J].Chinese Optics.doi:10.37188/CO.2023-0057
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The objective of this study is to explore the optimal detection location and the best prediction model of the suger level of Yongquan honey oranges, which can provide a theoretical basis for the brix measurement and classification of honey oranges. With the wavelength range of 390.2−981.3 nm hyperspectral imaging system was used to study the best position for detecting the sugar content of Yongquan honey oranges, and the spectral information of the calyx, fruit stem, equator and global of Yongquan honey oranges were combined with their sugar content of corresponding parts to establish its prediction model. The original spectra from the different locations were pre-processed by Standard Normal Variance (SNV) transformation, Multiple Scattering Correction (MSC), baseline calibration (Baseline) and SG smoothing, respectively, and the Partial Least Squares Regression (PLSR) and Least Squares Support Vector Machine (LSSVM) models were established based on the pre-processed spectral data. The best pre-processing methods for different parts of the honey oranges were found, and the optimal spectral data obtained by the best pre-processing methods were conducted to identify characteristic wavelengths using the Competitive Adaptive Re-weighting Sampling algorithm (CARS) and Uninformative Variable Elimination (UVE). Finally, the PLSR and LSSVM models were established and compared based on the selected spectral data. The results show that the global MSC-CARS-LSSVM model demonstrates the most accurate prediction performance, with a correlation coefficient of
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