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In this paper (open access) the authors used non-targeted nuclear magnetic resonance (NMR) in combination with principal components analysis followed by linear discriminant analysis (PCA-LDA) to build a classification model that discriminated fresh from frozen-thawed fish (mackerel, trout, cod). The optimum extraction and NMR method was chosen after evaluating 6 methods to investigate both the lipid fraction and the polar fraction of the fish samples. After cross-validation embedded in a Monte Carlo resampling design, six independent classification models were obtained. Evaluation of the multivariate data analysis revealed that the most promising approaches were the ¹H NMR analysis of the lipid fraction (correct prediction of about 90.0%) and the ¹H NMR based screening of minor components of the lipid fraction with a correct prediction of about 91.9%. ¹H NMR analysis of the water extract of the fish samples showed a correct prediction of about 82.6%. The authors conclude that a general differentiation of fresh from frozen-thawed fish via non-targeted NMR is feasible, even though the underlying sample batch contained different fish species. Additional fish samples need to be analyzed with the three most promising NMR approaches to further improve the developed classification models.

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