Title: Classification of Gilthead Sea Bream (Sparus aurata) from 1H NMR Lipid Profiling Combined with Principal Component and Linear Discriminant Analysis
Authors: REZZI SergeGIANI IvanHEBERGER KarolyAXELSON DavidMORETTI VittorioRENIERO FABIANOGUILLOU CLAUDE
Citation: JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY vol. 55 no. 24 p. 9963-9968
Publisher: AMER CHEMICAL SOC
Publication Year: 2007
JRC N°: JRC41866
ISSN: 0021-8561
URI: http://pubs.acs.org/cgi-bin/article.cgi/jafcau/2007/55/i24/html/jf070736g.html
http://publications.jrc.ec.europa.eu/repository/handle/JRC41866
DOI: 10.1021/jf070736g
Type: Articles in Journals
Abstract: The combination of 1H 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 1H 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.
JRC Institute:Institute for Health and Consumer Protection

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