Metal complexation model identification and the detection and elimination of erroneous points using evolving least-squares fitting of voltammetric data

The experimental errors and their propagation are very often neglected when using voltammetric data for chemical model identification, consecutive stability-constants determination and speciation studies. The influence of the experimental error has been analyzed for two mathematical models used very frequently, viz. the Leden–DeFord–Hume and the van Leeuwen mathematical models. It was demonstrated using simulated noise-free and noise-corrupted data that relatively small and usually occurring experimental errors, in half-wave or peak potential, can blur the initially assumed chemical model or can lead to a set of incorrect stability constants. In order to minimize the influence of error on model identification and parameter estimation, an evolving least-squares fitting (ELSQF) procedure is proposed which makes use of progressively increasing (in forward and backward directions) data window size. At the same time, a procedure for detection and elimination of erroneous points is introduced enabling more reliable estimation of the parameters that describe the metal-ion–ligand complexation systems.