Application of random forests to select premium quality vegetable oils by their fatty acid composition

Abstract

In order to discriminate premium quality from inexpensive edible oils, the fatty acid profiles of tea, rapeseed, corn, sunflower and sesame oil were compared with the ones from extra virgin olive oil (EVOO). Fatty acid methyl esters were quantified by GC/MS. Principal component analysis (PCA) and random forests (RF) were applied to cluster the samples. RF showed a better ability of discrimination and also revealed the contribution of each variable to the clustering model. The multidimensional scaling (MDS) plot of the RF proximity matrix demonstrated that tea oil was similar to EVOO. Meanwhile, it was observed that the total content of cis-monounsaturated fatty acids (79.48%) in tea oil was close to EVOO (80.71%), especially the oleic acid (77.38% and 77.45%, respectively). The results suggest that tea oil might be a good edible oil choice, considering the high oleic acid content and similar fatty acid profiles compared to those of EVOO.

Keywords: Fatty acids; GC/MS; Random forests; Vegetable oils.