13C NMR (nuclear magnetic resonance) spectroscopy, in conjunction with multivariate analysis of commercial fish oil-related health food products, have been used to provide discrimination concerning the nature, composition, refinement, and/or adulteration or authentication of the products. Supervised (probabilistic neural networks, PNN) and unsupervised (principal component analysis, PCA; Kohonen neural networks; generative topographic mapping, GTM) pattern recognition techniques were used to visualize and classify samples. Simple PCA score plots demonstrated excellent, but not totally unambiguous, class distinctions, whereas Kohonen and GTM visualization provided better results. Quantitative class predictions with accuracies >95% were achieved with PNN analysis. Trout, salmon, and cod oils were completely and correctly classified. Samples reported to be salmon oils and cod liver oils did not cluster with true salmon and cod liver oil samples, indicating mislabeling or adulteration.
The aim of this study was to use 13 C-nuclear magnetic resonance (NMR) regiospecific analyses of triacylglycerols to distinguish fish oils from different fish species for authentication purposes. 13 C-NMR data of muscle lipids from Atlantic salmon (Salmo salar L.), mackerel (Scomber scombrus) and herring (Clupea harengus) were obtained, and the distribution of omega-3 polyunsaturated fatty acids between the sn-1,3 and sn-2 glycerol chains calculated from the carbonyl region. The results show that there were significant differences in the sn-2 position specificity of the fatty acids 22:6n-3, 20:5n-3 and 18:4n-3 among the species investigated. The most pronounced difference was that herring had a higher proportion of its 22:6n-3 in the sn-2 position compared to the two other species. Principal component analysis of data points in the carbonyl-region showed that there were also differences in the level and regiospecific distribution of monounsaturated/saturated fatty acids, which made it possible to distinguish oils of these three species solely from the carbonyl region of 13 C-NMR spectra.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.