Classification of edible vegetable oils based on ¹H-NMR spectroscopy and PCA-SVM

To establish a method for the classification of edible oils by ¹H-NMR spectroscopy and PCA-SVM and to compare its effectiveness with that of SIMCA. First,the PCA method was used to reduce the dimensionality of independent variables. Then the first two principal components were selected as input variables of the support vector machine(SVM),based on the established PCA-SVM prediction model. The seven kinds of oils could be identified by the proposed technique. The results revealed that the value of g and c were 1.7411 and 0.3299,respectively,which were optimized by grid method. The accuracy of prediction could reach to 100% with the PCA-SVM model,while that was only 61.90% with SIMCA model. It was validated by results that the combination of ¹H-NMR spectroscopy with PCA-SVM could achieve the classification of edible oils quickly and effectively.