Non -Destructive Detection of Soluble Solids in Cherry Tomatoes by Visible/Near Infrared Spectroscopy Based on SG-CARS-IBP

The content of soluble solids (SSC) plays an essential role in the internal quality of cherry tomatoes. However, SSC detection has some problems based on hyperspectral imaging and dielectric properties. There are few SSC non-destructive testing models for cherry tomatoes currently. Therefore, in order to realize the non-destructive detection of SSC in cherry tomatoes, a prediction model of internal quality based on the spectral characteristics of cherry tomatoes and an improved BP neural network algorithm were proposed to solve the problem of rapid non-destructive detection of cherry tomatoes' internal quality. In this study, cherry tomatoes were selected as the research object, and there were 188 test samples divided into a training set of 150 and a testing set of 38. The cherry tomatoes' reflective intensity in 350-1 000 nm was obtained using the visible/near-infrared spectral acquisition system, and corrected sample reflectivity was obtained and analyzed. The practical information of the cherry tomatoes' spectral in 481. 15-800. 03 nm was intercepted to enhance the signal-to-noise ratio. A BP neural network prediction model was established by comparing the effective wavelengths treated by Savitzky-Golay smoothing (SG). The coefficient of determination (R2) and root mean square error (RMSE) for the test set were 0. 578 5 and 0. 563 9. On this basis, the network structure of the BP neural network was improved to seek the optimal prediction structure of the BP neural network. The error between the output layer and the expected value was calculated. The network structure parameters were adjusted, and the learning rate and the number of neurons were set to 0. 01 and 5 to establish BP neural network model (SG-IBP). The R2 and RMSE of the test set were 0. 981 2 and 0. 102 3. While the R2 and RMSE of the test set were 0. 997 8 and 0. 047 9, with 18 feature lengths screened by the competitive adaptive reweighted sampling algorithm (CARS). Meanwhile, the speed was greatly improved. The results showed that the performance of the improved BP neural network model was significantly improved. After feature lengths were extracted by CARS, R2 of the test set was increased by 0. 419 3, and RMSE was reduced by 0. 516. The speed was also significantly improved. Therefore, the improved BP neural network model, which used CARS to extract characteristic lengths (SG-CARS-IBP), had apparent advantages, and the SG-CARS-IBP model was more suitable for studying cherry tomatoes' SSC non-destructive detection. This study can provide a reference for efficient non-destructive detection of cherry tomatoes.