Data enhancement in adsorptive stripping voltammetry by the application of digital signal processing techniques

Abstract

Adsorptive stripping voltammetry (AdSV) is a useful trace electroanalytical technique. However, it suffers from a number of inherent problems that, inevitably, limit the applicability and scope of the technique. The most significant of these drawbacks are sloping baselines, dissolved oxygen in the sample, low signal-to-noise ratios, lack of linearity at higher concentrations and unresolved stripping peaks. By monitoring the stripping response in the digital domain, it is possible to apply various digital signal processing (DSP) operations in order to improve the quality of the data in AdSV. In this work, a number of DSP techniques were applied to data obtained by AdSV; the operations employed included background subtraction, ensemble averaging, digital filtering (both in the time and frequency domain), multiple scanning methods and deconvolution. Other parameters relevant to the treatment of the data (such as the sampling frequency) were also considered. It was found that, after careful optimization of the parameters, the analytical utility of the data was improved; however, caution and knowledge are required to avoid either inappropriate application of a DSP technique or false interpretation of the results that might degrade the reliability of the data.