Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method.
A multicomponent analysis method based on principal component analysis-artificial neural network models (PC-ANN) is proposed for the determination of phenolic compounds. The method relies on the oxidative coupling of phenols (phenol, 2 chlorophenol, 3-chlorophenol and 4-chlorophenol) to N,N-diethyl-p-phenylenediamine in the presence of hexacyanoferrate(III). The reaction monitored at analytical wavelength 680 nm of the dye formed. Phenols can be determined individually over the concentration range 0.1-7.0 microg ml(-1). Differences in the kinetic behavior of the four species were exploited by using PC-ANN, to resolve mixtures of phenol. After reducing the number of kinetic data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. The optimized ANN allows the simultaneous quantitation of four analytes in mixtures with relative standard errors of prediction in the region of 5% for four species. The results show that PC-ANN is an efficient method for prediction of the four analytes.
Hasani
Masoumeh
M
Faculty of chemistry, Bu-Ali Sina University, Hamedan 65174, Iran. hasani@basu.ac.ir
Moloudi
Mahsa
M
D004785:Environmental Pollutants / Q000032:analysis*; D005292:Ferricyanides / Q000737:chemistry; D006863:Hydrogen-Ion Concentration; D007700:Kinetics; D016571:Neural Networks, Computer; D010636:Phenols / Q000032:analysis*; D010655:Phenylenediamines / Q000737:chemistry; D025341:Principal Component Analysis; D012680:Sensitivity and Specificity; D013056:Spectrophotometry, Ultraviolet
D004785:Environmental Pollutants; D005292:Ferricyanides; D010636:Phenols; D010655:Phenylenediamines; C013935:N,N-diethyl 4-phenylenediamine; C028033:potassium ferricyanide