A combination of chemometrics methods and GC-MS for the classification of edible vegetable oils

The authenticity of edible vegetable oils is a very important issue due to consumer health and commercial reasons. Gas chromatography-mass spectrometry (GC-MS) was applied to analyze the fatty acid composition of sixty six samples from six different kinds of edible vegetable oils. The fatty acid profiles of these edible vegetable oils were used to classify the type of edible oils. For improving the classification accuracy of vegetable oils with respect to type, the support vector machine (SVM) technique, optimized using the genetic algorithm (GA), was employed to construct the classification model. The effectiveness of the GA-SVM combination in classification was compared with that of other well-known strategies for classification, such as minimum distance dassification (MDC) and linear discriminant analysis (LDA). In addition, the Kennard-Stone algorithm was used to select the representative training samples and compared with the random sampling method. The misclassification rates were 8.48% and 3.03% for training and test set, respectively, by the GA-SVM model using the linear kernel. Only one or two samples will be misclassified in the process of GA-SVM classification. The classification task based on fatty acid data can be successfully achieved by the GA-SVM technique combined with the Kennard-Stone algorithm. The results reveal that this strategy is of great promise in flexible and accurate classification of edible vegetable oils. (C) 2016 Elsevier B.V. All rights reserved.