Study on the Effect of Moisture Content on the Spectral Detection of Soluble Solids in Apricot (Prunus armeniaca ‘Diao Gan')

To reduce the influence of moisture content variation on the spectral detection of soluble solid content (SSC) and achieve rapid, non-destructive detection of SSC, as well as optimize the processing parameters of apricot products, improve detection efficiency and quality control, this study, based on spectral detection technology, combined with the multiple scatter correction (MSC) preprocessing method, concentration residual method for outlier removal, competitive adaptive reweighted sampling method for feature band selection and partial least squares (PLS) method, constructed SSC detection models for apricots at different moisture contents, and explored the impact of moisture content on the detection model effect of SSC in apricot. This study showed that as moisture content decreased, both the absolute values of the valley depth and valley area in the first-derivative spectrum near 970 nm, as well as the peak height and peak area of the reflection peak, gradually diminished, indicating a weakening of moisture characteristics. Furthermore, within the 450 nm-1450 nm wavelength range, SSC prediction accuracy significantly improved with decreasing moisture content. Under 8-hour apricot drying conditions, the model achieved optimal prediction accuracy with an R _p of 0.9844, RMSEP of 0.5712, and RPD of 5.7989, meeting high-precision quantitative requirements. This study discovered the influence of different moisture intervals on the hyperspectral SSC prediction model of apricot fruits, laying a foundation for the subsequent research on moisture content correction spectra to eliminate the influence of moisture content on spectral detection of SSC content.