Optimization and compensation of models on tomato soluble solids content assessment with online Vis/NIRS diffuse transmission system

This study developed an efficient and compensable model for predicting soluble solids content (SSC) of tomato, based on the self-developed online Vis/NIRS diffuse transmission system. The fruit stem-calyx axis horizontal coupled with suitable light settings (path and intensity) was determined as the best measurement parameters, which significantly reduce the stray light and also optimize the light propagation inside tomato. The pretreatment method of Savitzky-Golay smoothing (SGS) combined with multiplicative scatter correction (MSC) could eliminate the spectral difference between samples and the inherent system noise in the raw spectral of tomato. The partial least squares regression (PLSR) model based on the 22 key wavelengths selected by competitive adaptive reweighted sampling (CARS) had better model performance than the full wavelength model. Finally, the CARS-PLSR model was further optimized by compensating physiological traits of height and weight with Rp of 0.91 and RMSEp of 0.17%. Our results in the present study demonstrated the potential of using online Vis/NIRS diffuse transmission spectra combined with model optimization and compensation as a valuable method for tomato SSC assessment.