title: Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.
abstract: Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network.
authors.affiliation: Department of Chemistry, Faculty of Science, Yazd University, Yazd 89195-741, Iran. Electronic address: abenvidi@yazd.ac.ir.
authors.forename: Ali
authors.initials: A
authors.lastname: Benvidi
authors.affiliation: Department of Chemistry, Faculty of Science, Yazd University, Yazd 89195-741, Iran.
authors.forename: Saleheh
authors.initials: S
authors.lastname: Abbasi
authors.affiliation: Laboratory of Chemoinformatics et al. Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
authors.forename: Sajjad
authors.initials: S
authors.lastname: Gharaghani
authors.affiliation: Department of Chemistry, Faculty of Science, Yazd University, Yazd 89195-741, Iran.
authors.forename: Marzieh
authors.initials: M
authors.lastname: Dehghan Tezerjani
authors.affiliation: Department of Analytical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Islamic Republic of Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Masoum
mesh_terms: D000465:Algorithms; D001391:Azo Compounds / Q000032:analysis*; D003116:Color; D009282:Naphthalenesulfonates / Q000032:analysis*; D016571:Neural Networks, Computer; D013053:Spectrophotometry / Q000379:methods*; D013057:Spectrum Analysis; D013645:Tartrazine / Q000032:analysis*
chemical_list: D001391:Azo Compounds; D009282:Naphthalenesulfonates; C100258:methyl orange; C005842:6-hydroxy-5-((p- sulfophenyl)azo)-2-naphthalenesulfonic acid disodium salt; D013645:Tartrazine; C576297:ponceau 4R
keywords: Artificial neural network; Food colorants; Hybrid of partial swarm optimization-Genetic algorithm; Multicomponent analysis