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