Signal denoising and baseline correction by discrete wavelet transform for microchip capillary electrophoresis

Signal denoising and baseline correction using discrete wavelet transform (DWT) are described for microchip capillary electrophoresis (MCE). DWT was performed on an electropherogram describing a separation of nine tetramethylrohodamine-5-isothiocyanate labeled amino acids, following MCE with laser-induced fluorescence detection, using Daubechies 5 wavelet at a decomposition level of 6. The denoising efficiency was compared with, and proved to be superior to, other commonly used denoising techniques such as Fourier transform, Savitzky-Golay smoothing and moving average, in terms of noise removal and peak preservation by directly visual inspection. Novel strategies for baseline correction were proposed, with a special interest in baseline drift that frequently occurred in chromatographic and electrophoretic separations.