Comparative Analysis of Non-Destructive Prediction Model of Soluble Solids Content for Malus micromalus Makino Based on Near-Infrared Spectroscopy

To investigate the feasibility of using near-infrared (NIR) spectral technology to detect the soluble solids content (SSC) of Malus micromalus Makino, rapid and non-destructive prediction models of SSC were studied using least-square support vector regression (LS-SVR), partial least squares regression (PLSR), and the error back propagation artificial neural network (BP-ANN). First, 110 samples of NIR diffuse reflectance spectra in the wavelength range of 400.41-1083.89 nm were obtained, and then were divided into the calibration set and prediction set by sample set partitioning based on the joint x-y distance (SPXY) algorithm. Second, we compared the prediction performance of the PLSR model after preprocessing by nine spectral preprocessing methods, and applied data dimension reduction methods (random frog, the successive projections algorithm (SPA), and principal component analysis) for variable selection. Finally, the effect of applying full spectrum and characteristic spectrum modeling on SSC prediction accuracy was compared and analyzed. The comparison studies confirmed that the optimal fusion model of SPA-LS-SVR had the best performance (R-C = 0.9629, R-P = 0.9029, RMSEC = 0.199, RMSEP = 0.271). The experimental results could provide a reference for future development of the internal component analysis system for Malus micromalus Makino based on NIR spectroscopy and its classification system using SSC as the classification standard.