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A variety of recovered-edible-oil identification models were established by using Raman combined with near-infrared spectroscopy (NIR). Eight types of 156 edible vegetable oil samples were collected to acquire their Raman and NIR spectra. The spectral data were processed for modeling. The preprocessing methods for the Raman spectra included the moving average method (11 points), adaptive iterative reweighted-penalty least squares method, and the normalization method based on the intensity of the characteristic peak at 1454 cm-1 (MA11-airPLS-Nor). The preprocessing method for the NIR spectra was the standard normal variable transformation algorithm combined with a detrending technique (SNV_DT). The Raman and NIR spectra were fused at the feature level by using independently the serial fusion and wavelet fusion approaches. The results showed that with the serial-fusionand wavelet-fusion-based models, the identification of recovered oils can be achieved very rapidly. Furthermore, the comprehensive performances of the models based on fused Raman and NIR data were better than those of models based on separate Raman or NIR data. Keywords—vegetable oil; spectral fusion; Raman spectroscopy; near-infrared spectroscopy

Rapid Identification and Characterization of Recovered Edible Oil, Based on Raman and Near-Infrared Spectroscopy