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Deviations from bilinearity in multivariate voltammetric calibration models.

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.