DATA SMOOTHING IN STRIPPING VOLTAMMETRY BY SIMPLEX FUNCTION FITTING

Stripping voltammetry (SV), in common with all trace voltammetric anal ytical techniques, suffers from poor signal-to-noise (S/N) ratios due to external interference pick up and inherent generation of noise in t he electrochemical system. In addition to other methods developed to s mooth noisy data (such as time and frequency domain filtering), optimi sation methods can also be used to reject the noise present in electro chemical measurements. In this paper, an optimisation method, based on a modified Simplex procedure, is presented. The method is based on mi nimising the deviation (in terms of the mean square error) between the recorded data and a theoretical model of the data. In this case, the model was a response surface consisting of one or more peak-shaped fun ctions of one of the following types: Gaussian (G), exponentially modi fied Gaussian (EMG), Lorentzian (L), combined Gaussian-Lorentzian (GL) and theoretical voltammetric peak (TVP). The method was successfully applied to stripping signals obtained for the trace quantification of riboflavin and for the simultaneous determination of zinc, cadmium lea d and copper by SV.