Abstract
A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.
Highlights
Microfluidic technology, especially microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) [1,2,3], has become a very important and promising branch of miniaturized total chemical analysis systems (μ-TAS) [4,5,6]
It is difficult to filter by optimizing the hardware design of the ME-C4D equipment. e threshold method based on wavelet transform has a good denoising performance. erefore, this paper studies the appropriate denoising method based on wavelet transform
A method of ME-C4D signal denoising based on an improved threshold function of wavelet transform is proposed. e simulation experiment results suggest that the proposed method in this paper is superior to Savitzky–Golay, Complexity
Summary
Microfluidic technology, especially microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) [1,2,3], has become a very important and promising branch of miniaturized total chemical analysis systems (μ-TAS) [4,5,6]. Erefore, it can effectively avoid some troubles of electrochemical contact detect method, such as electrode scaling, electrolysis bubble, electric field interference, and so on [13,14,15,16]. E denoising effect of this method mainly depends on the selection of threshold function. Some traditional threshold functions such as hard threshold and soft threshold are widely used for signal denoising due to the simple structure and good efficiency. Zhang et al improve the soft threshold function to enhance the electrochemiluminescence CE signal denoising
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