Rapid Detection of Adulteration in Extra Virgin Olive Oil by Low-Field Nuclear Magnetic Resonance Combined with Pattern Recognition

Abstract

Intentional addition of cheaper oils into olive oil (OL) for economic motivation has been becoming particularly attractive due to the favorable flavor and healthy characteristics of OL, but it is very challenging to identify such adulteration because of the compositional similarity between the oils. In this study, low-field nuclear magnetic resonance (LF-NMR) in combination with multivariate statistical analysis was used to identify the adulterated olive oil with different rations of soybean oil (SO) or corn oil (CO). Significant differences in multi-component relaxation time (T21 and T22) and peak area proportions (S21 and S22) were detected between pure and adulterated OL. As the adulteration ratio increased, S21 and S22 changed linearly, while T21 and T22 only changed slightly. The detection by gas chromatography suggested that T21 and T22 values might be influenced by triacylglycerol components, and the changes of S21 and S22 were attributed to the varied mono-/polyunsaturated fatty acids. In the relaxation time-based pattern recognition models, the authentic OL could be correctly identified from the adulterated ones with at least 20% of SO or CO by principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA). The multi-blended oil could be 100% classified by orthogonal partial least squares discriminant analysis (OPLS-DA) and 98.8% classified by principal component analysis followed by linear discriminant analysis (PCA-LDA) when the adulteration ratio was above 30%, demonstrating a promising technique of LF-NMR combined with pattern recognition in rapid screening of the edible oils.