Abstract
Flaxseed oil is popular edible oil and an important additive in functional foods and feeds. Recently, economically motivated adulteration as a type of oil fraud becomes emerging risk. In this study, the fatty acid profiles of flaxseed oil were analyzed by gas chromatography-mass spectrometry operating in selected ion monitoring mode and then used to detect adulterated flaxseed oil with the help of multivariate statistical methods including principle component analysis (PCA), and recursive support vector machine (R-SVM). The detection results indicate that the discriminant model built with 28 fatty acids can identify adulterated flaxseed oil samples (10%) with high accuracy of 95.6%. Therefore, fatty acid profiles based adulteration detection for flaxseed oil is an important strategy for preventing customers far from adulterated flaxseed oil.
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