Application of principal component-wavelet neural network in spectrophotometric determination of acidity constants of 4-(2-thiazolylazo)-resorcinol.

The acidity constants of 4-(2-thiazolylazo)-resorcinol (TAR) were determined by the principal component-wavelet neural network (WNN). Biprotic acid mass balance equations, the distribution functions and the corresponding spectral profiles which were generated by a Gaussian model, have been considered to simulate all required absorbance-pH data. The simulated absorption-pH data matrix was used as training set whereas the TAR absorption-pH data was used as the test set of WNN model. The obtained acidity constants were in good agreement with the reported values of acidity constants in the literature and with those calculated by DATAN software. Artificial neural network (ANN) model has been also employed in this study and the results of WNN were compared with those obtained by ANN. It was found that WNN gives faster convergence and slightly better accuracy.