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.

Spectrophotometric resolution of ternary mixtures of tryptophan, tyrosine, and histidine with the aid of principal component-artificial neural network models.

A simple and sensitive spectrophotometric method to resolve ternary mixtures of tryptophan (Trp), tyrosine (Tyr), and histidine (His) in synthetic and water samples is described. It relies on the different kinetic rates of the analytes in their oxidative reaction with potassium ferricyanide (K(3)Fe(CN)(6)) in alkaline medium. The absorbance data were monitored on the analytical wavelength (420 nm) of K(3)Fe(CN)(6) spectrum. Synthetic mixtures of the three amino acids were analyzed, and the data obtained were processed by principal component-artificial neural network (PC-ANN) models. After reducing the number of spectral data using principal component analysis, an artificial neural network consisting of three layers of nodes was trained by applying a back-propagation learning rule. Tangent and sigmoidal transfer function were used in the hidden and output layers, respectively. The analytical performance of this method was characterized by relative standard error. The method allowed the determination of Trp, Tyr, and His at concentrations between 10 and 55, 10 and 60, and 10 and 40 microg ml(-1), respectively. The results show that the PC-ANN is an efficient method for prediction of the three analytes.