Discriminant analysis of vegetable oils by thermogravimetric-gas chromatography/mass spectrometry combined with data fusion and chemometrics without sample pretreatment

Abstract

This paper describes the feasibility of thermogravimetric-gas chromatography/mass spectrometry (TGA-GC/MS) combined with data fusion and chemometrics for discrimination of various vegetable oils (soybean, rapeseed, peanut, sunflower, olive and camellia) and determination of adulteration of olive oil with soybean oil. Separate and fused data of TGA and GC/MS analysis of various vegetable oils were subjected to chemometric analyses by principal component analysis (PCA) and linear discriminant analysis (LDA). The results demonstrate that data fusion can effectively enhance the discrimination power of PCA-LDA model, with a prediction rate of 96.2% being obtained at the optimum weight ratio of GC/MS: TGA (0.9: 0.1). The developed method was applied to the determination of adulteration of olive oil with soybean oil, allowing for distinguishment between olive oil samples and adulterated oil samples with adulteration levels ranging from 25% to 75%, and a prediction rate of 100% was achieved. The newly developed method based on TGA-GC/MS technique in combination with data fusion provides a new idea for the authentication of vegetable oils.