An overlapping peak separation algorithm based on multiorder differential method and genetic algorithm for magnetic eddy current signal of a defect cluster

Rationale: Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely.Methods: It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time.Results: A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%.Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness.Conclusions: This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS.

Section: Introductionmentioning

confidence: 99%

An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm

Gao

et al. 2021

“…However, the accuracy of these methods is overly dependent on the choice of model parameters and on the degree of overlap of the overlapping components. IPSO prevents local optimization by destroying particles and iterating on optimal solutions 26 . The algorithm has high precision for the IMS overlapping peak deconvolution.…”

Section: Introductionmentioning

confidence: 99%

“…IPSO prevents local optimization by destroying particles and iterating on optimal solutions. 26 The algorithm has high precision for the IMS overlapping peak deconvolution. However, it cannot effectively solve the local optimum problem, which requires manual tuning and long time for continuous research and is not suitable for practical applications.…”

mentioning

confidence: 99%

Deconvolution of overlapping peaks in ion mobility spectrometry based on a multiobjective dynamic teaching‐learning‐based optimization

Tang

Bao

et al. 2022

Rationale Because of its powerful analytical ability, ion mobility spectrometry (IMS) plays an important role in the field of mass spectrometry. However, one of the main defects of IMS is its low structural resolution, which leads to the phenomenon of peak overlap in the analysis of compounds with similar mass charge ratio. Methods A multiobjective dynamic teaching‐learning‐based optimization (MDTLBO) method was proposed to separate IMS overlapping peaks. This method prevents local optimization and identifies peak model coefficients efficiently. In addition, the position information of particles largely reflects the half‐peak width of IMS, which makes single peaks difficult to appear and coefficient identification easier. Results The performance comparison of MDTLBO with other deconvolution methods (genetic algorithm, improved particle swarm optimization algorithm, and dynamic inertia weight particle swarm optimization algorithm) shows that the maximum deconvolution error of MDTLBO is only 0.7%, which is much lower than that for the other three methods. In addition, robustness is a performance index that reflects the advantages and disadvantages of the algorithm. Conclusion MBTLBO is more robust than other algorithms for separating overlapping peaks. The algorithm can separate the heavily overlapped mobility peaks, produce better analysis results, and improve the resolution of IMS.

“…[35][36][37] The genetic algorithm (GA) has a strong peak separation ability in severely overlapped area, 38 and an overlapping peaks separation method algorithm based on the GA improved the precision of separation. 39 The separation accuracy of the above described methods, however, depend excessively on the selection of model parameters, degree of overlap of overlapping peaks, and so on. In addition, the results provided by these methods are unstable or even may not converge in the analysis of strongly overlapped peaks.…”

Section: Introductionmentioning

confidence: 99%

“…Different improved methods based on WT have achieved good results in the enhancement of resolution of overlapping peaks 35–37 . The genetic algorithm (GA) has a strong peak separation ability in severely overlapped area, 38 and an overlapping peaks separation method algorithm based on the GA improved the precision of separation 39 . The separation accuracy of the above described methods, however, depend excessively on the selection of model parameters, degree of overlap of overlapping peaks, and so on.…”

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

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.