A structural approach for smoothing noisy peak-shaped analytical signals

This work describes an approach for smoothing noisy peak-shaped analytical data based on the identification of the structural form of the signal. The data series was described first as a succession of `peak structuresʹ and then as a succession of `meta-peak structuresʹ. This description enabled convenient identification of the characteristic peaks arising from an analytical measurement and their separation from the noise components in the data. The method was applied to both voltammetric and spectroscopic data featuring different distributions of noise in the frequency domain. It was demonstrated that the suggested structural approach is successful in identifying the characteristic peaks with precision and, subsequently, in smoothing the test signals. The smoothing operation relying on the structural approach is fast, and, in contrast to traditional smoothing techniques, the fine detail of the signals is retained and no artefacts are generated as a result of the smoothing operation.