Classification and Adulteration Detection of Vegetable Oils Based on Fatty Acid Profiles

TLDR

Adulteration of high‑priced oils poses a significant food‑quality and safety concern. The study aims to develop an authenticity detection method to protect consumer health. Fatty acid profiles of five edible oils were obtained by GC/MS with selected‑ion monitoring, and 28 fatty acids were used in PCA, hierarchical clustering, and random‑forest models to classify oils and to build a Monte‑Carlo‑based adulteration detection model. The models accurately classified the five oils and detected adulteration as low as 10 %.

Abstract

The detection of adulteration of high priced oils is a particular concern in food quality and safety. Therefore, it is necessary to develop authenticity detection method for protecting the health of customers. In this study, fatty acid profiles of five edible oils were established by gas chromatography coupled with mass spectrometry (GC/MS) in selected ion monitoring mode. Using mass spectral characteristics of selected ions and equivalent chain length (ECL), 28 fatty acids were identified and employed to classify five kinds of edible oils by using unsupervised (principal component analysis and hierarchical clustering analysis), supervised (random forests) multivariate statistical methods. The results indicated that fatty acid profiles of these edible oils could classify five kinds of edible vegetable oils into five groups and are therefore employed to authenticity assessment. Moreover, adulterated oils were simulated by Monte Carlo method to establish simultaneous adulteration detection model for five kinds of edible oils by random forests. As a result, this model could identify five kinds of edible oils and sensitively detect adulteration of edible oil with other vegetable oils about the level of 10%.