engleski

Signal processing and experimental design in electroanalysis

In electroanalysis, the main focus of signal processing is the transformation of measured data, in order to conveniently extract the appropriate information (e.g. peak intensity). Such mathematical transformation should minimize the distortion of the original information as much as possible. The most frequently employed signal processing routines are the noise removal (digital smoothing), the baseline correction, the signal intensity determination, the overlapped peaks separation, and various ways of mathematical manipulations (e.g. differentiation, integration). Usually, voltammetric curves are treated one at the time using the best “decision maker” method known as the “expert eye”. The produced effect depends mainly on experience of the operator and the capabilities of the software used. Once the sequence of treatment procedures is defined, an automatic processing method could be applied. Having this option available within the software, would be of great advantage for electroanalytical applications with a large number of curves. However, software packages supplied with electrochemical instrumentation usually does not provide such a functionality (or it is very limited), and researchers are referred to specialized independent data treatment software packages. The signal-to-noise ratio and the signal-to-background relationships are the two major voltammetric curve characteristics that should be improved after signal processing. Digital filtering (smoothing) and baseline corrections are applied for this purpose. These two steps become more important as the electrochemical analysis approaches to the limit of quantification of the applied method (Fig. 1). Transformation of voltammetric curves by a derivative function is an optional approach that can eliminate the need to use the baseline correction method, which is user dependent (subjective). However, it is very important for the user to identify the capabilities and limitations of the used processing routines, as an inappropriate use could result in incorrect final results. Various effects produced by signal processing and mathematical transformations on the final result, will be demonstrated through examples of determination of unknown analyte concentration using the standard addition method. Several options for the improvement of analysis accuracy and precision, based on the internal standard method, will be presented. The consequences of improper signal processing in environmental studies will be illustrated on the example of determination of copper complexing capacity. Guidelines of signal processing and the experimental design will be provided.

signal processing; electroanalysis; voltammetry

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