Classification of Vegetable Oils Based on Graphical Presentation and Bivariate Discriminant Node Model

A novel method for classification of vegetable oil samples by fatty acid composition is proposed. It is based on classification trees with bivariate linear discriminant node model, which can simultaneously reduce tree size, improve class prediction, and enhance data visualization. Exploratory analysis by parallel coordinate plot indicates outliers exist in oil samples. Linear discriminant analysis (LDA) is sensitive to outliers; consequently when it is applied to 96 samples of known vegetable oil classes, three oil samples are misclassified. This paper considers a nonparametric alternative to LDA: classification trees with bivariate linear discriminant node model. The classification tree method is found to be a good model for the classification of different edible vegetable oils. Compared with other methods such as counterpropagation neural network, this method has merits of robust to outliers, easy interpretation and enhanced interactive data visualizing