title: Classification of wild and farmed salmon using bayesian belief networks and gas chromatography-derived fatty acid distributions.
abstract: 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.
authors: MRi_Consulting, Kingston, Ontario, Canada.
authors: David E
authors: DE
authors: Axelson
authors: Inger B
authors: IB
authors: Standal
authors: Iciar
authors: I
authors: Martinez
authors: Marit
authors: M
authors: Aursand
mesh_terms: D000818:Animals; D017756:Aquaculture; D001499:Bayes Theorem; D002849:Chromatography, Gas; D005227:Fatty Acids / Q000032:analysis*; D005515:Food Labeling; D020317:Salmo salar / Q000145:classification*; D012680:Sensitivity and Specificity
chemical_list: D005227:Fatty Acids