Near-Infrared Model and Its Robustness as Affected by Fruit Origin for ‘Dangshan’ Pear Soluble Solids Content and pH Measurement

Soluble solid content (SSC) and acidity (pH) are two important factors indicating the fruit quality of pears can be measured by near-infrared spectroscopy (NIRS). However, robustness these measurements as affected different origins remains largely unknown. In this study, we developed an NIRS method to measure ‘Dangshan’ pear (Pyrus spp.) SSC pH evaluated non-destructive detection examining effects from three in 2019 2020. First, Kennard–Stone was used divide calibration set 2020 samples orchards. The partial least squares (PLS) model establish local origin hybrid models predict pears’ pH. Second, a combination competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination (UVE) implemented construct spectral prediction based on effective variables for assessing origins. results showed that produced large errors predicting origins, model, established performed better than other models. Finally, could effectively simplified using 70 52 characteristic selected CARS method. Pear harvested orchards were independent verify validity variables. predicted R2p more 0.9 0.85, respectively. This finding indicates difference has influence quantitative inversion measurements, constructed improve accuracy. These findings provide theoretical basis development rapid devices