Rapid prediction of fatty acid composition of vegetable oil by Raman spectroscopy coupled with least squares support vector machines

The fatty acid composition of vegetable oil plays a significant role in a nutrition-balanced diet, which makes this industry more quality conscious. A set of store-purchased vegetable oils and their binary mixtures were characterized by Raman spectra in a region of 800-2000cm(-1). The obtained Raman spectral data were pretreated, and intensities of eight characteristic peaks were extracted as the eigenvalues of an entire spectrum. A prediction model of fatty acid content based on least squares support vector machines (LS-SVM) were established for multivariate analysis between the Raman spectral eigenvalues and the fatty acid composition measured by gas chromatography (GC) method. The performance of the model was evaluated by comparing the predicted values to the reference values from GC analysis. The correlation coefficient for the prediction of oleic acid, linoleic acid and -linolenic acid was 0.9972, 0.9982 and 0.9854, respectively. Raman spectroscopy based on LS-SVM can be a promising technique for predicting the fatty acid composition of vegetable oil with the advantages of being simple and time-effective while not requiring any sample preprocessing. In particular, a portable Raman system is suitable for on-site detection in practical applications. Copyright (c) 2013 John Wiley & Sons, Ltd.