The utility of Fourier transform near-infrared spectroscopy to identify geographical origins of Chinese pears

Consumer trust in fruit industry is partly depended on the labeling of its geographical origin. The accurate label is not only of great significance to consumers and retailers, but also for their brands shaping. This work evaluated the utility of Fourier transform near-infrared spectroscopy (FT-NIRS) in identifying the geographical origins of Chinese pears including Laiyang pear (Pyrus bretschneideri cv. Laiyang), Dangshan pear (Pyrus bretschneideri Rehd. cv. Dangshansuli), and Korla pear (Pyrus sinkiangensis T. T. Yu). FT-NIR spectra of 316 pears from three different geographical origins were obtained in the spectral range of 10,000-4000 cm(-1). Preliminary investigation was first conducted based on principal component analysis (PCA) to confirm the similar spectral features of samples within one class, and the results showed that PC1 and PC2 were effective for the identification. Partial least square discriminant analysis (PLS-DA) models based on full spectra indicated that the optimal classification based on second-order derivative (Der2) preprocessed spectra achieved 100% correct classification rates (CCRs) in all datasets. After that, the multivariate selection strategies including two-dimensional correlation spectroscopy (2D-COS), competitive adaptive reweighted sampling (CARS) combined with successive projection algorithm (SPA), and uninformative variable elimination (UVE) combined with SPA were individually optimized for selecting the characteristic wavenumbers from full FT-NIR spectra. On the basis of simplified PLS-DA models establishment, the CCRs in calibration, cross-validation, and prediction sets of the selected CARS-SPA-PLS-DA model achieved 98.58%, 98.11%, and 98.08%, respectively. This research demonstrated that FT-NIRS combined with multivariate analysis can be successfully employed to identify the geographical origins of Chinese pears.