Signal Processing Methods for Capillary Electrophoresis

Stewart, Robert L.; Gideoni, Iftah; Zhu, Yonggang

doi:10.5772/23446

Cited by 3 publications

(9 citation statements)

References 131 publications

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“…Various peak shape models and signal processing techniques have been reviewed for capillary electrophoresis, 18,19 including signal de-noising and baseline correction, both of which are common in electrophoretic separations. For example, peak tailing underestimates the diffusion coefficient, while sample overload and slow migration in highly concentrated samples lead to overestimation.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Accurate Determination of the Diffusion Coefficient of Proteins by Fourier Analysis with Whole Column Imaging Detection

Zarabadi

Pawliszyn

2015

Anal. Chem.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Accurate Determination of the Diffusion Coefficient of Proteins by Fourier Analysis with Whole Column Imaging Detection

Zarabadi

Pawliszyn

2015

Anal. Chem.

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“…In CE, the signal is usually modelled as a superposition of a number of components under the assumption of system linearity [15]. A typical signal model for CE is usually expressed as…”

Section: Signal Modelmentioning

confidence: 99%

“…The simulated CE signal (see Fig. 3a) was generated by using the model formulated in (15), specifically, five peaks, two types of baselines (linear baseline and exponential baseline). To make the simulated signal to be more general, an overlapped peak consisting of two separated peaks was also taken into consideration.…”

Section: Experiments On Simulated Signalmentioning

confidence: 99%

“…In CE, the signal is usually modelled as a superposition of a number of components under the assumption of system linearity [15]. A typical signal model for CE is usually expressed as

y false(t false) = \sum_{i = 1}^{K} p_{i} false(t false) + B false(t false) + n false(t false)

where

p_{i} false(t false)

is a peak which corresponds to the i th analyte, and

B false(t false)

and

n false(t false)

correspond to the baseline drifting and the noise, respectively.…”

Section: Signal Model and Denoising Algorithmmentioning

confidence: 99%

“…One of the simplest denoising strategy for CE signal based on WT is the soft or hard thresholding. The success of noise removal using the threshold relies on the fact that the underlying signal has a sparse representation, that is it is approximated by a small number of relatively large‐amplitude coefficients, whereas noise will have its energy spread across a large number of small‐amplitude coefficients [15]. Since the wavelet filter has a predefined structure that cannot match well with all the characteristics of any signals, a type of wavelet can achieve a good result for a given signal, and it will not achieve a satisfactory denoising performance for another different signal.…”

Section: Introductionmentioning

confidence: 99%

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Denoising method for capillary electrophoresis signal via learned tight frame

Wang

Gao

et al. 2020

IET signal process.

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Since capillary electrophoresis (CE) signals are always contaminated by random noise, which has negative influence on the accuracy of detection and analysis, it is necessary to remove noise before further applications of the CE signals. In this study, a tight frame learned from the data itself is applied to the removal of noise for CE signals. To achieve an effective decomposition of the CE signal, a one-dimensional discrete tight frame tailored to the input signal is first constructed by introducing tight frame constraint into the popular dictionary learning model. Then, due to each subband containing different information of the noise, an adaptive threshold is computed to shrink the detail coefficients instead of using a global threshold. Finally, the denoised CE signal is reconstructed from the thresholded coefficients by using the inverse transform of the tight frame. To evaluate the denoising efficiency, the proposed method is applied to the simulated CE signals and real CE signals. Experimental results indicate that compared with other denoising methods, the proposed method obtains a better shape preservation of the peaks as well as a higher signal-to-noise ratio.

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A signal analysis and identification scheme for an online multiphase micron-sized particle analyzer system

Wang¹,

Cao²,

Wang³

et al. 2021

Meas. Sci. Technol.

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Online microparticle detection is of utmost importance for industrial production. This paper proposes a signal processing and feature identification strategy to achieve particle size statistics for online measurement in a three-phase stirred tank reactor based on the electrical sensing zone (ESZ) method. Signal denoising and de-interference are achieved using the wavelet soft threshold method combined with mathematical morphological filtering. Pulse selection is implemented using pulse width limiting conditions. The key features that distinguish the pulse waveforms are defined based on the differences in the motion characteristics of the different types of particles through the aperture. Finally, the unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles. The results show that the particle size distribution identified by this strategy agrees with offline measurements indicating the effectiveness of the scheme. The effects of electromagnetic noise and the interference of small bubbles that approximate the particle size in solution in the online, in-situ measurement task are solved. This study scheme has a guiding and facilitating role in applying the ESZ principle to the industrial online measurement environment.

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Signal Processing Methods for Capillary Electrophoresis

Cited by 3 publications

References 131 publications

Accurate Determination of the Diffusion Coefficient of Proteins by Fourier Analysis with Whole Column Imaging Detection

Accurate Determination of the Diffusion Coefficient of Proteins by Fourier Analysis with Whole Column Imaging Detection

Denoising method for capillary electrophoresis signal via learned tight frame

A signal analysis and identification scheme for an online multiphase micron-sized particle analyzer system

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