Continuous Wavelet Transform to Improve Resolution of Overlapped Peaks Based on Curve Fitting

Li, Yuan-lu; Wang, Qi; Sun, Ning; Zhou, Wei; Liu, Chunhui

doi:10.1080/00387010.2012.762403

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…They are presented in Table I, along with related information and references to methods with full automation potential. 39,–118…”

Section: Theorymentioning

confidence: 99%

“…95,126,129 However, peak parameter estimation requires initial estimates of the very same parameters; these can be difficult to gauge, 94 and unless guesses are very good, variations in initial parameters can lead to widely varying results. 93,130 The requisite initial estimates can be based on derivatives, 94 principal component analysis, 99 two-dimensional peak finding, 93 biologically inspired procedures, 92,100 monitoring changes in fitting errors as a function of minimum separable peak-peak distances, 95 and wavelet transforms, 97 among others. Both Lorentzian distributions (also known as Student's t-distributions with one degree of freedom or Cauchy distributions or Breit-Wigner distributions) and Gaussian distributions have been used for vibrational band curve fitting.…”

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confidence: 99%

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Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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High-throughput information extraction from large numbers of Raman spectra is becoming an increasingly taxing problem due to the proliferation of new applications enabled using advances in instrumentation. Fortunately, in many of these applications, the entire process can be automated, yielding reproducibly good results with significant time and cost savings. Information extraction consists of two stages, preprocessing and analysis. We focus here on the preprocessing stage, which typically involves several steps, such as calibration, background subtraction, baseline flattening, artifact removal, smoothing, and so on, before the resulting spectra can be further analyzed. Because the results of some of these steps can affect the performance of subsequent ones, attention must be given to the sequencing of steps, the compatibility of these sequences, and the propensity of each step to generate spectral distortions. We outline here important considerations to effect full automation of Raman spectral preprocessing: what is considered full automation; putative general principles to effect full automation; the proper sequencing of processing and analysis steps; conflicts and circularities arising from sequencing; and the need for, and approaches to, preprocessing quality control. These considerations are discussed and illustrated with biological and biomedical examples reflecting both successful and faulty preprocessing.

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“…They are presented in Table I, along with related information and references to methods with full automation potential. 39,–118…”

Section: Theorymentioning

confidence: 99%

mentioning

confidence: 99%

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Schulze

Turner

2015

Appl Spectrosc

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“…A series of improvements according to curve fitting has been widely tried in separating IMS overlapping peaks. [30][31][32] Mathematical differentiation is a second derivative peak detection algorithm that shows good performance in the separation of overlapping IMS peaks. 33 In the two-dimensional IMS dataset, mathematical differentiation is combined with the characteristics of surfaces curvature to perform separation of severely overlapping peaks.…”

Section: Introductionmentioning

confidence: 99%

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm

Zhang

Xie

et al. 2020

Rapid Comm Mass Spectrometry

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Rationale Ion mobility spectrometry (IMS) is a powerful analytical tool that has been widely applied in many fields. However, the limited structural resolution of IMS results in peak overlapping in the analysis of samples with similar structures. We propose a novel method, improved particle swarm optimization (IPSO), for the separation of IMS overlapping peaks. Methods This method, which prevents local optimization, is used to identify the peak model coefficients of IMS. Moreover, we use the half‐peak width characteristics of IMS to determine the particle position range, which eliminates impossible combinations of single peaks and reduces the difficulty of identification of coefficients. Results During a comparison in performance between IPSO and the genetic algorithm (GA), the results show that the maximum separation error of IPSO is only 1.45%, while the error of the GA is up to 17.43%. Moreover, the time consumed by IPSO is 95% less than that of the GA, and IPSO has a greater robustness under the same separation error conditions. Conclusions The method proposed provides accurate analytical results in separating overlapping IMS peaks even in cases of severe overlaps, which greatly enhances the structural resolution of IMS.

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“…So, the detail part was extracted by constructed LOD filter that could determine the geometric defects of the curve, resulting in a faired Class-A curve. Li et al 4 proposed an improved curve fitting method for the resolution of overlapping peaks. By continuous wavelet transform (WT) of sharp peaks, the reasonable initial values of each peak parameter were obtained to guarantee the curve accuracy on improved fitting conditions.…”

Section: Introductionmentioning

confidence: 99%

Rapid wavelet construction of multi-resolution fairing for curves and surfaces with any number of control vertices

2020

Advances in Mechanical Engineering

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In the field of curves and surfaces fairing, arbitrary resolution wavelet fairing algorithm made wavelet fairing technology widely extended to general curves and surfaces, which are determined by any number of control vertices. Unfortunately, accurate wavelet construction algorithm for general curves and surfaces still has not been perfect now. In this article, a concrete algorithm for reconstruction matrix and wavelet construction was emphatically studied, which would be used in the multi-resolution fairing process for curves and surfaces with any number of control vertices. The essence of this algorithm is to generalize wavelet construction into the solution of null space, which could be solved gradually and rapidly by the procedures of decomposition and simplification of coefficient matrix. Certainly, the related compactly supported wavelets could be constructed efficiently and accurately, too. In the last of the article, a complex curve and a complex surface case were provided to verify the stability, high performance, and robustness of this algorithm.

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Continuous Wavelet Transform to Improve Resolution of Overlapped Peaks Based on Curve Fitting

Cited by 14 publications

References 17 publications

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Development and Integration of Block Operations for Data Invariant Automation of Digital Preprocessing and Analysis of Biological and Biomedical Raman Spectra

Resolution enhancement of overlapping peaks of ion mobility spectrometry based on improved particle swarm optimization algorithm

Rapid wavelet construction of multi-resolution fairing for curves and surfaces with any number of control vertices

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