Classification of gilthead sea bream (Sparus aurata) from 1H NMR lipid profiling combined with principal component and linear discriminant analysis.

The combination of (1)H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed (1)H NMR profiles made a clear distinction between wild and farmed samples. Linear discriminant analysis (LDA) allowed classification of samples according to the geographic origin, as well as for the wild and farmed status using both PCA scores and NMR data as variables. Variable selection for LDA was achieved with forward selection (stepwise) with a predefined 5% error level. The methods allowed the classification of 100% of the samples according to their wild and farmed status and 85-97% to geographic origin. Probabilistic neural network (PNN) analyses provided complementary means for the successful discrimination among classes investigated.