Rapid Fatty Acids Detection of Vegetable Oils by Raman Spectroscopy Based on Competitive Adaptive Reweighted Sampling Coupled with Support Vector Regression

The composition and content of fatty acids are critical indicators of vegetable oils quality. To overcome the drawbacks of traditional detection methods, Raman spectroscopy was investigated for the fast determination of fatty acids composition in oil. Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected, and an eighth-degree polynomial function was used to fit the Raman spectrum. Then, the multivariate scattering correction, standard normal variable transformation (SNV), and Savitzky-Golay convolution smoothing methods were compared. Polynomial fitting combined with SNV was found to be the optimal pretreatment method. Characteristic wavelengths were selected by competitive adaptive reweighted sampling. For monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and saturated fatty acids (SFAs), 44, 75, and 92 characteristic wavelengths of rapeseed oil, and 60, 114, and 60 characteristic wavelengths of soybean oil were extracted. Support vector regression was used to establish the prediction model. The R 2 values of the prediction results of MUFAs, PUFAs, and SFAs for rapeseed oil were 0.9670, 0.9568, and 0.9553, and the RMSE values were 0.0273, 0.0326, and 0.0340, respectively. The R 2 values of the prediction results of fatty acids for soybean oil were respectively 0.9414, 0.9562, and 0.9422, and RMSE values were 0.0460, 0.0378, and 0.0548, respectively. A good correlation coefficient and small RMSE value were obtained, indicating the results to be highly accurate and reliable. Raman spectroscopy, based on competitive adaptive reweighted sampling coupled with support vector regression, can rapidly and accurately analyze the fatty acid composition of vegetable oil.