Nondestructive detection of the soluble solids content in apples based on Vis-NIR spectral fusion and GA-VIP-PLS

The soluble solids content (SSC) of an apple is a critical indicator determining its internal quality and market value. This study proposes a rapid and nondestructive method for apple SSC detection by integrating full visible-near-infrared (Vis-NIR) spectral information. The systematic combination of visible region and NIR region could achieve informational complementarity, with particular focus on the key "red region" spectra bond, which is highly relevant to SSC and physically interpretable. In spectra analysis, classical partial least squares (PLS) regression is adopted to handle spectral multicollinearity, coupled with an innovative genetic algorithm optimized variable importance in projection (GA-VIP) method for feature wavelength selection. VIP ensures the selected wavelengths are physically interpretable, particularly focusing on the red-edge region (∼700nm) highly correlated with SSC, while GA globally optimizes the VIP threshold (converging to 1.492 after 16 generations) rather than using a fixed cutoff. This approach achieves a 90.7% reduction in wavelength count while maintaining predictive accuracy. Our proposed method outperformed the full-spectrum approach and other conventional feature selection techniques, achieving a coefficient of determination (R²) of 0.9523, root mean square error (RMSE) of 0.146%, residual predictive deviation (RPD) of 2.31, ratio of performance to quantile intervals (RPQI) of 2.93, and bias of 0.0181%. More importantly, the method proposed here can be extended to rapid spectral detection in other agricultural products and food safety fields, holding substantial practical application value.