Universal Modeling for Non-Destructive Testing of Soluble Solids Content in Multi-Variety Blueberries Based on Hyperspectral Imaging Technology

The soluble solids content (SSC) of blueberry is a key index for evaluating its quality. In view of the demand for rapid non-destructive testing of blueberry SSC and the shortcomings of the existing single-variety testing models in cross-variety applications, a universal prediction model construction method based on hyperspectral imaging (HSI) technology is proposed in this study. The spectral data of three blueberry varieties were obtained by using a 935∼1720 nm hyperspectral imaging system. A partial least squares regression (PLSR) model was constructed by combining different preprocessing methods such as Savitzky–Golay (S-G), multiplicative scatter correction (MSC) and standard normal variable transformation (SNV). The results showed that the PLSR model pretreated by S-G-MSC-SNV had the best performance, and the determination coefficient, root mean square error and residual prediction deviation of the prediction set were 0.94, 0.33% and 3.94, respectively. The characteristic wavelengths were optimized in stages by uninformative variables elimination (UVE) and the successive projections algorithm (SPA), and the model was simplified by multiple linear regression (MLR). Finally, a high-precision UVE-PLSR model and a simple and efficient UVE-SPA-MLR hybrid model were obtained. The construction of this universal model effectively solves the limitation of the single-variety model and has important application value in the optimization of food industry production and quality control.