Modeling method for SSC prediction in pomelo using Vis-NIRS with wavelength selection and latent variable updating

The aim of this study was in-line, rapid, and non-destructive detection for soluble solid content (SSC) in pomelos using visible and near-infrared spectroscopy (Vis-NIRS). However, the large size and thick rind of pomelo affect the stability of spectral acquisition and the biological variabilities affect the robustness of models. Given these issues, in this study, an efficient prototype in-line detection system in transmittance mode was designed and evaluated in comparison with an off-line detection system. Data from the years 2019 and 2020 were used for modeling and the external validation data were obtained by the in-line detection system in 2021. The wavelength selection methods of changeable size moving window (CSMW), random frog (RF), and competitive adaptive reweighted sampling (CARS) were used to improve the prediction accuracy of partial least squares regression (PLSR) models. The best performance of internal prediction was obtained by CARS-PLSR and the determination coefficient of prediction (R_p^2 ), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were 0.958, 0.204%, and 4.821, respectively. However, all models obtained large prediction biases in external validation. The latent variable updating (LVU) method was proposed to update models and improve the performance in external validation. Ten samples from the external validation set were randomly selected to update the models. Compared with the recalibration method, LVU could effectively modify the original models which matched the SSC range of the external validation set. The CSMW-PLSR models were more robust in external validations. The off-line model with LVU performed best with a root mean square error of validation (RMSEV) of 0.599% and the in-line model with recalibration obtained RMSEV of 0.864%. These results demonstrated the application potential of the transmittance Vis-NIRS for in-line rapid prediction of SSC in pomelos and the modeling and updating methods could be applied to samples with biological variabilities.