Resolution of highly overlapping differential pulse anodic stripping voltammetric signals using multicomponent analysis and neural networks

This paper reports and discusses the results obtained by using multicomponent analysis methods based on multiple linear regression and neural network procedures to resolve highly overlapping signals obtained by differential pulse anodic stripping voltammetry by using a static drop electrode. The former procedures were applied to the well-known chemical model composed of Pb(II), Tl(I), In(III) and Cd(II) in binary, ternary and quaternary mixtures. Different network architectures are investigated using the back propagation algorithm. Versatile software for data processing was developed. The proposed methodology was used to determine these four metals in tap water.