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As constantes de acidez do PAR foram determinadas por Análise de Componentes Principais em Rede Neural Artificial, usando-se dados espectrais simulados e experimentais. As equações de balanço de massa do ácido triprótico e os perfis espectrais correspondentes, gerados por um modelo Gaussiano, foram usados para simular todos os dados de absorbância-pH necessários. Um ruído constante, com média zero e diferentes desvios padrões (1-3% dos valores máximos de absorbância) foi sobreposto ao espectro simulado gerado. Um modelo experimental triangular foi usado para selecionar e determinar as diferentes constantes de acidez simuladas. Os efeitos de ruído em diferentes níveis foram também estudados para avaliar a habilidade de predição do modelo. Um conjunto completo de dados experimentais de titulação fotométrica do PAR, no intervalo de pH 1,50-13,00, foi usado como teste. As constantes de acidez obtidas concordam com os valores previamente relatados usando-se o programa DATAN. 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.

A new application of PC-ANN in spectrophotometric determination of acidity constants of PAR