Integrating HS-SPME-GCMS with chemometrics for identifying adulterated flaxseed oils and tracing origins of additives

Abstract

The widespread occurrence of adulteration in the flaxseed oil requires urgent and efficient methods for addressing oil adulteration. In this study, we proposed a new strategy for efficiently identifying adulterated flaxseed oil and tracing botanical origin of illegally added oils. A data analysis guided parameter optimization was designed for head space solid-phase microextraction. Volatile compounds were then analyzed with gas chromatography-mass spectrometry, whose data files were processed with our automatic untargeted data analysis software, AntDAS-GCMS. The flavor difference between pure flaxseed oil and the other types of oils like soybean oil, peanut oil, and rapeseed oil, were comprehensively studied. Finally, a two-stage classification strategy was conducted to identify the adulterated flaxseed oils and, meanwhile, tracing botanical origins of additives, which was accomplished by designing two independent partial least squares-discrimination (PLS-DA) models. Results indicated that the developed strategy can obtain an accuracy of 100 % for identifying adulterated flaxseed oils by the first PLS-DA in the first stage and another accuracy of 96.0 % for tracing botanical origins of added oils by the other PLS-DA in the second stage. In conclusion, the developed data analysis strategy may provide a new solution for oil adulteration.