A GC-MS based Fatty Acid Profiling Approach for Uncovering the Composition of Edible Oil Blends

Vegetable oil blending has become more common lately. The oils are being blended whether to achieve better oil quality and health benefits or market frauds. The aim of this paper was to group binary oil blends based on the botanical origin of the oil components. For that reason, oil samples were prepared by blending extra-virgin olive oil (EVOO) and high-oleic sunflower oil (HOSO) (blend type I), as well as by blending flaxseed oil (FO) and sunflower oil (SO) (blend type II). Fatty acid methyl esters (FAMEs) present in the simulated blend samples and the pure oil samples were analyzed by gas chromatography coupled to a mass spectrometric detector (GC-MS). The ions of prominent FAMEs were extracted from total ion current (TIC) chromatograms and integrated. Peak percentages were used to create a matrix for further data processing. A heatmap was used as a hierarchical clustering tool to separate the blend samples into clusters according to the belonging botanical origin of each oil component in a blend. The simulated blends and pure oil samples were treated by two different multivariate analysis methods: heatmaps and principal component analysis (PCA), thereby dividing the samples into two groups according to the botanical origin of the blends. GC-MS analysis coupled with multivariate statistic tools was shown to be a useful approach to authenticate edible vegetable oils based on FAMEs composition.

Automated summary

Vegetable oil blending has become more common, serving purposes of improving oil quality and health benefits or committing market frauds. This study classified binary oil blends based on the botanical origin of the oil components, using GC-MS analysis and multivariate statistical tools. The results showed that this approach is useful for authenticating edible vegetable oils.