title: Patterns of PCDDs and PCDFs in human milk and food and their characterization by artificial neural networks.
abstract: Artificial neural network (ANN) has been recently introduced as a tool for data analysis. In this study, Kohonen's self-organizing maps (SOMs), a special type of neural network, were applied to a set of PCDD/PCDF concentrations found in 54 human milk and 83 food samples, which were collected in a number of countries all over the world. Data were obtained from the scientific literature. The purpose of the study was to find a potential relationship between PCDD/PCDF congener profiles in human milk and the dietary habits of the different countries in which samples were collected. The comparison of the SOM component planes for human milk and foodstuffs indicates that those countries with a greater fish consumption show also higher PCDD/PCDF concentrations in human milk. SOMs enable both the visualization of sample units and the visualization of congener distribution.
authors.affiliation: Laboratory of Toxicology and Environmental Health, School of Medicine, Rovira i Virgili University, San Lorenzo 21, 43201 Reus, Spain.
authors.forename: M
authors.initials: M
authors.lastname: Nadal
authors.forename: G
authors.initials: G
authors.lastname: Espinosa
authors.forename: M
authors.initials: M
authors.lastname: Schuhmacher
authors.forename: J L
authors.initials: JL
authors.lastname: Domingo
mesh_terms: D000465:Algorithms; D001572:Benzofurans / Q000032:analysis*; D016000:Cluster Analysis; D000072338:Dibenzofurans, Polychlorinated; D004147:Dioxins / Q000032:analysis*; D005506:Food Contamination / Q000032:analysis*; D006801:Humans; D008895:Milk, Human / Q000737:chemistry*; D016571:Neural Networks, Computer
chemical_list: D001572:Benzofurans; D000072338:Dibenzofurans, Polychlorinated; D004147:Dioxins