title: Artificial neural network approach to modelling of metal contents in different types of chocolates.
abstract: The relationships between the contents of various metals in different types of chocolates were studied using chemometric approach. Chemometric analysis was based on the application of artificial neural networks (ANN). ANN was performed in order to select the significant models for predicting the metal contents. ANN equations, that represent the content of one metal as a function of the contents of other metals were established. The statistical quality of the generated mathematical models was determined by standard statistical measures and cross-validation parameters. High agreement between experimental and predicted values, obtained in the validation procedure, indicated the good quality of the models. The obtained results indicate the possibility of predicting the metal contents in different types of chocolate.
authors.forename: Sanja
authors.initials: S
authors.lastname: Podunavac-Kuzmanović
authors.forename: Lidija
authors.initials: L
authors.lastname: Jevrić
authors.forename: Jaroslava
authors.initials: J
authors.lastname: Švarc-Gajić
authors.forename: Strahinja
authors.initials: S
authors.lastname: Kovačević
authors.forename: Ivana
authors.initials: I
authors.lastname: Vasiljević
authors.forename: Isidora
authors.initials: I
authors.lastname: Kecojević
authors.forename: Evica
authors.initials: E
authors.lastname: Ivanović
mesh_terms: D000535:Aluminum / Q000032:analysis*; D002099:Cacao / Q000737:chemistry*; D003300:Copper / Q000032:analysis*; D007854:Lead / Q000032:analysis*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009532:Nickel / Q000032:analysis*
chemical_list: D007854:Lead; D003300:Copper; D009532:Nickel; D000535:Aluminum