Discrete wavelets transform for signal denoising in capillary electrophoresis with electrochemiluminescence detection

Publisher Website
Google Scholar
Abstract
Discrete wavelets transform (DWT) was applied to noise on removal capillary electrophoresis‐electrochemiluminescence (CE‐ECL) electropherograms. Several typical wavelet transforms, including Haar, Daublets, Coiflets, and Symmlets, were evaluated. Four types of determining threshold methods, fixed form threshold, rigorous Stein's unbiased estimate of risk (rigorous SURE), heuristic SURE and minimax, combined with hard and soft thresholding methods were compared. The denoising study on synthetic signals showed that wave Symmlet 4 with a level decomposition of 5 and the thresholding method of heuristic SURE‐hard provide the optimum denoising strategy. Using this strategy, the noise on CE‐ECL electropherograms could be removed adequately. Compared with the Savitzky‐Golay and Fourier transform denoising methods, DWT is an efficient method for noise removal with a better preservation of the shape of peaks.