[Study on Brand Discrimination of Differential Oil Using Near-Infrared Spectroscopy with Different Resolutions]

The performance and influence of near-infrared spectroscopy with different resolutions for brand discrimination of differential oil was studied. Spectral data with resolutions of 4, 8, 16 and 32 cm(-1) were collected, and 10522.28-4443.425 cm(-1) spectra were analyzed after the removal of the absolute noises. The principal component analysis (PCA) of the spectral data at different resolutions indicated that the brands of differential oil could be discriminated. Patial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) models were built based on the full spectra with different resolutions. The discrimination results showed that all models obtained similar and good performances with the classification rate over 90%, and the best results were obtained when the model was built based on the full spectra with the resolution of 8 cm(-1). Successive projections algorithm was applied to select effective wavenumbers, and different effective wavenumbers were selected for the spectra with different resolutions. PLS-DA and SVM models based on the selected effective wavenumbers both obtained good results. Their results were similar to the models established based on full spectra. The results indicated that spectral resolution had little effect on the discrimination results, and spectral resolution effected the selection of effective wavenumbers, and spectral resolution should be taken into consideration for the selection of effective wavenumbers in practical applications. In all, near-infrared spectroscopy with different resolutions and the corresponding selected effective wavenumbers could be used for discrimination of differential oil brands.