Deconvolution of overlapping peaks in ion mobility spectrometry based on a multiobjective dynamic teaching‐learning‐based optimization. (14th November 2022)

• Record Type: Journal Article
• Authors: Tang, Xu; Yu, Jiangcheng; Xie, Zhijun; Tang, Keqi; Hu, Shifu; Li, Jun; Wu, Yong
• Is Part Of: Rapid communications in mass spectrometry. Volume 37:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-14
• Subjects: Mass spectrometry -- Periodicals; 543.65
• DOI: 10.1002/rcm.9379
• Languages: English
• ISSNs: 0951-4198

Abstract

Abstract : 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.