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Abstract

Support vector machines (SVMs) were used as a novel learning machine in the authentication of the origin of salmon. SVMs have the advantage of relying on a well-developed theory and have already proved to be successful in a number of practical applications. This paper provides a new and effective method for the discrimination between wild and farm salmon and eliminates the possibility of fraud through misrepresentation of the country of origin of salmon. The method requires a very simple sample preparation of the fish oils extracted from the white muscle of salmon samples. 1H NMR spectroscopic analysis provides data that is very informative for analysing the fatty acid constituents of the fish oils. The SVM has been able to distinguish correctly between the wild and farmed salmon; however ca. 5% of the country of origins were misclassified.

Details

Title
Application of support vector machines to 1H NMR data of fish oils: methodology for the confirmation of wild and farmed salmon and their origins
Author
Masoum Saeed 1 ; Malabat Christophe 2 ; Jalali-Heravi Mehdi 1 ; Guillou, Claude 3 ; Rezzi Serge 4 ; Rutledge, Douglas Neil 5 

 Sharif University of Technology, Department of Chemistry, Tehran, Iran (GRID:grid.412553.4) (ISNI:0000000107409747) 
 Laboratoire de Chimie Analytique, Paris, France (GRID:grid.412553.4) 
 Physical and Chemical Exposure Unit, European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Ispra (VA), Italy (GRID:grid.434554.7) (ISNI:0000 0004 1758 4137) 
 Physical and Chemical Exposure Unit, European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Ispra (VA), Italy (GRID:grid.434554.7) (ISNI:0000 0004 1758 4137); BioAnalytical Science, Metabonomics and Biomarkers, Nestlé Research Center, Lausanne 26, Switzerland (GRID:grid.419905.0) (ISNI:0000 0001 0066 4948) 
 Laboratoire de Chimie Analytique, Paris, France (GRID:grid.419905.0) 
Pages
1499-1510
Publication year
2007
Publication date
Feb 2007
Publisher
Springer Nature B.V.
ISSN
16182642
e-ISSN
16182650
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2665374571
Copyright
© Springer-Verlag 2007.