Abstract
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.
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