The acidity constants of the PAR were determined by Principal Component Analysis Artificial Neural Networks, using simulated and experimental spectral data. Triprotic acid mass balance equations and corresponding spectral profiles generated by a Gaussian model were used to simulate all required absorbance-pH data. A constant noise with zero mean and different standard deviations (1-3% of the maximum absorbance values) was superimposed on the generated simulated spectra. A triangular experimental design was used to select and produce the different simulated acidity constants. The effects of white noise at different levels were also studied to check the prediction ability of the model. A fully experimental data set, photometric titration data of PAR at pH=1.50-13.00 range was used as a test set. The obtained acidity constants are in a good agreement with previously reported values using DATAN software.
acidity constants; experimental design; Principal Component Analysis; Artificial Neural Networks; photometric titration; DATAN
