Determination of the Soluble Solids Content in Korla Fragrant Pears Based on Visible and Near-Infrared Spectroscopy Combined With Model Analysis and Variable Selection.

Abstract

The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the application of portable devices is more common. The construction of an accurate and stable prediction model is the key for the successful application of the device. In this study, the visible and near-infrared (Vis/NIR) spectra of Korla fragrant pears were collected by a commercial portable measurement device. Different pretreatment methods were used to preprocess the raw spectra, and the partial least squares (PLS) model was constructed to predict the SSC of pears for the determination of the appropriate pretreatment method. Subsequently, PLS and least squares support vector machine (LS-SVM) models were constructed based on the preprocessed full spectra. A new combination (BOSS-SPA) of bootstrapping soft shrinkage (BOSS) and successive projections algorithm (SPA) was used for variable selection. For comparison, single BOSS and SPA were also used for variable selection. Finally, three types of models, namely, PLS, LS-SVM, and multiple linear regression (MLR), were constructed based on different input variables. Comparing the prediction performance of all models, it showed that the BOSS-SPA-PLS model based on 17 variables obtained the best SSC assessment ability with rp of 0.94 and RMSEP of 0.27 °Brix. The overall result indicated that portable measurement with Vis/NIR spectroscopy can be used for the detection of SSC in Korla fragrant pears.