Lipid classification of fish oil omega-3 supplements by 1H NMR and multivariate analysis

Abstract

The worldwide advent of concentrated supplements containing omega-3 fatty acids (FA) in the form of triacylglycerols (TAG) or ethyl esters (EE) has increased the interest in developing methods to classify these products. The quality control based on their lipid composition has become necessary since EE bioavailability has been proved to be lower when compared to the TAG. In this preliminary study, eight models based on 1H NMR and supervised discriminant analysis (PLS-DA/OPLS-DA) were applied to classify omega-3 fish oil in TAG or EE forms. The 4.0–4.5 ppm region was selected for modeling since it bracketed spectral features to discriminate TAG and EE. The non-supervised principal component analysis was employed to visually evaluate the distribution of samples and revealed a clear separation of TAG from EE marine oils along PC1. In addition, representative TAG and EE samples were 100 % correctly classified using any of the eight supervised models studied. The developed models resulted in high R2Y (≥ 0.977) and Q2 (≥ 0.953), and low root mean square error for prediction (≤ 0.009), which demonstrates the high potential of this rapid and straightforward procedure to evaluate the lipid form of supplements and mislabeling.