Classification of Wild and Farmed Salmon Using Bayesian Belief Networks and Gas Chromatography-Derived Fatty Acid Distributions

In this study, we present the use of Bayesian Belief Networks (BBN) for the classification of wild versus farmed Atlantic salmon (Salmo salar L.). Using a data set of 131 salmon samples from several geographical origins and the gas chromatography-derived distributions of 12 fatty acids (FAs), a Baye...

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Bibliographic Details
Published in:Journal of agricultural and food chemistry 2009-09, Vol.57 (17), p.7634-7639
Main Authors: Axelson, David E, Standal, Inger B, Martinez, Iciar, Aursand, Marit
Format: Article
Language:English
Subjects:
accuracy
Analytical Methods
Animals
Aquaculture
Bayes Theorem
Bayesian theory
Biological and medical sciences
Chromatography, Gas
classification
farmed fish
fatty acid composition
Fatty Acids - analysis
Fish and seafood industries
food analysis
food composition
Food industries
Food Labeling
Fundamental and applied biological sciences. Psychology
gas chromatography
General aspects
geographical variation
Methods of analysis, processing and quality control, regulation, standards
prediction
Salmo salar
Salmo salar - classification
salmon
Sensitivity and Specificity
wild foods
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Summary:In this study, we present the use of Bayesian Belief Networks (BBN) for the classification of wild versus farmed Atlantic salmon (Salmo salar L.). Using a data set of 131 salmon samples from several geographical origins and the gas chromatography-derived distributions of 12 fatty acids (FAs), a Bayesian Belief Network was constructed, ultimately using only the three most important FAs (16:1n-7, 18:2n-6, and 22:5n-3). The training data set yielded a prediction error of 0% (68/68 farmed; 20/20 wild correct) while the validation data set prediction error was 4.65% (32/32 farmed; 9/11 wild correct). Different randomly chosen validation sets yielded similar prediction accuracies. This model was then applied to 30 market (store-bought) samples where predictions were compared with the product labels.
ISSN:0021-8561
1520-5118
DOI:10.1021/jf9013235