Nondestructive detection of mango soluble solid content in hyperspectral imaging based on multi-combinatorial feature wavelength selection

This paper explores the prediction of the soluble solid content (SSC) in the visible and near-infrared (400-1,000 nm) regions of Baise mango. Hyperspectral images of Baise mangoes with wavelengths of 400-1,000 nm were obtained using a hyperspectral imaging system. Multiple scatter correction (MSC) was chosen to remove the effect of noise on the accuracy of the partial least squares (PLS) regression model. On this basis, the characteristic wavelengths of mango SSC were selected using the competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), uninformative variable elimination (UVE), and combined CARS + GA-SPA, CARS + UVE-SPA, and GA + UVE-SPA characteristic wavelength methods. The results show that the combined MSC-CARS thorn GA-SPA-PLS algorithm can reduce redundant information and improve the computational efficiency, so it is an effective method to predict the SSC mangoes.