A hyperspectral imaging technique for rapid non-destructive detection of soluble solid content and firmness of wolfberry

Soluble solid content (SSC) and firmness are significant indexes to evaluate the quality of wolfberry. This study employed hyperspectral imaging (HSI) technology for the rapid detection and visualization of the distribution of SSC and firmness in mature wolfberries. The hyperspectral images of Ningqi 1 and Ningqi 7 were collected in the range of 400–1000 nm. The image segmentation method was used to determine the region of interest (ROI) of the wolfberry samples and extract the mean spectra, and the performance of the four preprocessing techniques was evaluated based on the partial least squares (PLSR) model, which concluded that the standard normal variable transformation (SNV) and multiple scattering correction (MSC) preprocessing methods were able to achieve the optimal results. Principal component analysis (PCA), successive projection algorithm (SPA), competitive adaptive reweighted sampling method (CARS) and their combination were used to select the characteristic wavelength, with CARS-SPA being more accurate. PLSR, support vector machine regression (SVR) and backpropagation genetic algorithm (BPNN-GA) models were used to predict the soluble solid content and firmness of wolfberry by full wavelength and characteristic wavelength, respectively. The optimal model for SSC and firmness of Ningqi 1 was identified as MSC-CARS-SPA-BPNN-GA, with R_p² of 0.949 and 0.913, RMSEP of 0.365 and 0.524, and RPD of 4.104 and 3.422, respectively. For Ningqi 7, the optimal model was SNV-CARS-SPA-BPNN-GA, with R_p² of 0.936 and 0.880, RMSEP of 0.364 and 0.537, and RPD of 3.860 and 2.706, respectively. Finally, these optimal models were utilized to visualize the distribution of SSC and firmness in the ROI. The findings underscore the rapid and precise nature of hyperspectral imaging in detecting the SSC and firmness of wolfberry, thereby establishing a technological and theoretical foundation for expedited wolfberry quality assessment.