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A new algorithm for background correction in voltammetry based on Discrete Wavelet Transform (DWT) conjugated with spline functions is presented. This procedure simplifies selection of the peak position and optimization of the approximation procedure parameters by lowing the resolution of the signal in the process of the wavelet decomposition. Background approximation by splines and its subtraction is performed in the wavelet domain. Likewise the elimination of high frequency components in signals is conducted simultaneously. The reliability of the results obtained in the standard addition method was tested on simulated curves with real background and Pb(II) signals registered in CRM.

This work considers the problem of lack of bilinearity in multivariate calibration. In voltammetry this issue especially relies on the analysis of overlapping signals, which change the shape, sensitivity or shift along the potential axis, causing a significant loss of linearity. It limits the quality of many chemometric models designed for linear data. Improvement of the predictive ability of multivariate calibration models is achieved by pre-processing of the raw data. In this work we proposed the application of a technique called orthogonal signal correction (OSC). We demonstrated that orthogonal correction enables the removal of almost all non-linear effects, disturbing voltammetric signals that impede the building of effective PLS models. The methodology was presented using simulated signals, and also in determination of the nanomolar concentration of scandium in the presence of a high and changing excess of nickel.