| Citation: | LI Bin, WAN Xia, LIU Ai-lun, ZOU Ji-ping, LU Ying-jun, YAO Chi, LIU Yan-de. Study on the optimal detection position of brix value of Yongquan honey tangerines 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 brix value of Yongquan honey tangerines, which can provide a theoretical basis for the brix measurement and classification of honey tangerines. With the wavelength range of 390.2−981.3 nm hyperspectral imaging system was used to study the best location for detecting the brix of Yongquan honey tangerines, and the spectral information of the calyx, fruit stem, equator and global of Yongquan honey tangerines were combined with their brix values of corresponding parts to establish its prediction model. The original spectra from the different locations were pre-processed by standard normal variance transformation (SNV), multiple scattering correction (MSC), baseline calibration (Baseline) and convolutional smoothing (SG), 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 tangerine were found, and the spectral data obtained from these pre-processing methods were analyzed using the competitive adaptive re-weighting algorithm (CARS) and uninformative variable elimination (UVE) to identify characteristic wavelengths. 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 Rp=0.955 and an RMSEP value of 0.395. Alternatively, the SNV-PLSR model of the equatorial location of honey tangerines was found to be the next more effective, with a correlation coefficient of Rp=0.936, and an RMSEP value of 0.37. The correlation coefficients of the two prediction models are similar, the equatorial location can be used as the optimal location for measuring the brix of honey tangerines. This study demonstrates that the use of varying segments of the orange impacts the accuracy of prediction models. Identifying the optimal location and prediction model can provide a theoretical basis for classifying oranges for brix testing.
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