Adaptive-degree polynomial filter for voltammetric signals

The adaptive-degree polynomial filter (ADPF) for data smoothing is further improvement of the least-squares regression formalism introduced to the experimental data processing by Savitzky and Golay. The adaptive approach bases on the statistical testing of the fitting quality of polynomial function to the experimental data in the smoothing window sliding along the curve. It allows the automatic choice of the proper degree of the polynomial function in the various parts of the experimental curve. The sets of the simulated according to the Feldberg model voltammetric curves reflecting calibration process are considered. The normal random noises of different level were added to simulated curves and next processed with ADPF and fixed-degree polynomial filter. The performance of smoothing methods were tested by means of the sum of squared errors, signal to noise ratio and improvement of the analytical parameters such as: detection limit, sensitivity, linearity and other.