Extending the dynamic range of copper determination in differential pulse adsorption cathodic stripping voltammetry using wavelet neural network.
A wavelet neural network (WNN) model is proposed for extending the dynamic range of Cu(II) determination by differential pulse adsorption cathodic stripping voltammetry (DP-AdSV) using xylenol orange (XO) as a suitable ligand. All of voltammograms data consisting of Cu(II) and Cu(II)-XO peak currents were used in WNN model. The WNN model consisted of three layers (2-8-1) with the Morlet mother wavelet transfer function in the hidden layer. The model was able to extend the dynamic range of Cu(II) from its narrow linear range (1-50 ng ml(-1)) to the higher dynamic range (1-1500 ng ml(-1)). The results of the WNN model was also compared with artificial neural network (ANN) model and it was demonstrated the superiority of the WNN model relative to ANN model.
Khayamian
T
T
College of Chemistry, Isfahan University of Technology, Isfahan, Iran.
Ensafi
Ali A
AA
Benvidi
A
A