Non-destructive testing of red globe grape sugar content and moisture content based on visible/near infrared spectroscopy transmission technology

In this paper, a non-destructive detection method for the sugar and moisture content of red globe grapes based on visible/near-infrared spectroscopy transmission technology is studied. The PLSR model is established by collecting 360 red globe grape samples by using spectral data processed by spectral preprocessing methods such as Standard Normal Variable transformation (SNV), SavitZky-Golay(S_G) and other spectral preprocessing methods respectively to determine the best spectral preprocessing method. Seven data dimensionality reduction methods of primary dimensionality reduction (GA, SPA, CARS, UVE) and secondary dimensionality reduction combinations (CARS-SPA, UVE-SPA, GA-SPA) are used to identify characteristic variables of spectra. PLSR and LSSVM detection models of sugar content and moisture content of red globe grape are established respectively, and the advantages and disadvantages of each model are compared and analyzed. The results show that the optimal PLSR model wavelength extraction method for red globe grape sugar content and moisture content is GA-SPA-PLSR, and the correlation coefficients of the optimal model are 0.958 and 0.938, respectively. The optimal LSSVM model wavelength extraction methods for red globe grape sugar and moisture content are CARS-SPA-LSSVM and UVE-SPA-LSSVM, respectively. The correlation coefficients of the optimal model are 0.969 and 0.942, respectively. The model built using LSSVM is better than that built using PLSR, but its operation time is longer. The results also show that the non-destructive detection method of red globe grape sugar and moisture content based on visible/near-infrared technology is feasible, and the detection accuracy of both two optimal detection models is high, which can meet detection requirements. Different models can be selected for different applications. The optimal model built by PLSR has shorter computation time and is suitable for online rapid detection. LSSVM has the best detection performance and can accurately detect red globe grape sugar and moisture content.