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Hao J, Feng R, Li J, Gao W, Yu J, Tang K. A high-performance standalone planar FAIMS system for effective detection of chemical warfare agents via TSPSO algorithm. Talanta 2024; 269:125516. [PMID: 38070286 DOI: 10.1016/j.talanta.2023.125516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024]
Abstract
A high-performance standalone planar field asymmetric waveform ion mobility spectrometry (p-FAIMS) system with a deconvolution algorithm (two-step particle swarm optimization algorithm, TSPSO) for overlapping peaks was developed to effectively detect chemical warfare agents (CWAs). Four CWA simulants were applied in this study to systemically evaluate the performance of the standalone p-FAIMS system. The experimental results showed that each CWA simulant in the mixture can be positively identified by carefully comparing the compensation voltage (CV) value of each peak in the FAIMS spectra for the mixture to the ones in the spectra acquired by using the same FAIMS system for the pure CWA simulant standards. The FAIMS spectrum of the CWA simulant mixture might consist of multiple overlapping peaks, which would be difficult to accurately determine the CV value for each CWA simulant peak. This problem has been effectively resolved in this study by deconvoluting the overlapping peaks via the TSPSO algorithm. As the effective peak deconvolution via TSPSO requires the degree of overlap between each FAIMS peak to be lower than a specific value, the flow rate of FAIMS carrier gas was decreased to further improve the resolution of the p-FAIMS system. After the accurate deconvolution, the resolution of original FAIMS spectrum can also be enhanced to achieve baseline separation by using TSPSO algorithm to narrow the peak width of each peak. The experimental results in this study demonstrated the possibility of using TSPSO algorithm to achieve high-resolution on a typically low-resolution standalone FAIMS. The concept in this study can potentially be applied to any low-resolution instruments to achieve high-resolution results.
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Affiliation(s)
- Jie Hao
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, PR China
| | - Rong Feng
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; School of Material Science and Chemical Engineering, Ningbo University, Ningbo, 315211, PR China
| | - Junhui Li
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; School of Material Science and Chemical Engineering, Ningbo University, Ningbo, 315211, PR China.
| | - Wenqing Gao
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; School of Material Science and Chemical Engineering, Ningbo University, Ningbo, 315211, PR China
| | - Jiancheng Yu
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, PR China
| | - Keqi Tang
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, 315211, PR China; Zhenhai Institute of Mass Spectrometry, Ningbo, 315211, PR China; School of Material Science and Chemical Engineering, Ningbo University, Ningbo, 315211, PR China.
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Hu X, Zhou J, Li J, Gao W, Zhou J, Yu J, Tang K. An improved algorithm for resolving overlapping peaks in ion mobility spectrometry and its application to the separation of glycan isomers. Analyst 2023; 148:5514-5524. [PMID: 37791632 DOI: 10.1039/d3an01042b] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Despite the popularity of ion mobility spectrometry (IMS) for glycan analysis, its limited structural resolution hinders the effective separation of many glycan isomers. This leads to the overlap of IMS peaks, consequently impacting the accurate identification of glycan compositions. To this end, an improved algorithm, namely second-order differentiation combined with a simulated annealing particle swarm optimization algorithm based on sine adaptive weights (DWSA-PSO), was proposed for the separation of overlapping IMS peaks formed by glycan isomers. DWSA-PSO first performed second-order differentiation to automatically determine the number of components in overlapping peaks and exclude impossible single-peak combinations. It then introduced sinusoidal adaptive weights and a simulated annealing mechanism to improve the algorithm's search capability and global optimization performance, thereby enabling accurate and efficient separation of individual peaks. To evaluate the performance of DWSA-PSO and its application to the separation of glycan isomers, multiple sets of overlapping peaks with different degrees of overlap were simulated, and various types of multi-component overlapping peaks were formed using six disaccharide and four trisaccharide isomers. The experimental results consistently demonstrated that the DWSA-PSO algorithm outperformed both the improved particle swarm optimization (IPSO) algorithm and the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm in terms of separation accuracy, running time, and fitness values. In addition, the DWSA-PSO algorithm was successfully applied to the separation of glycan isomers in malt milk beverage. All these results reveal the capability of the DWSA-PSO algorithm to facilitate the accurate identification of glycan isomers.
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Affiliation(s)
- Xiangyang Hu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, P. R. China.
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
| | - Junfei Zhou
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, P. R. China.
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
| | - Junhui Li
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
- Ningbo Zhenhai Institute of Mass Spectrometry, Ningbo, P.R. China
| | - Wenqing Gao
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
- Ningbo Zhenhai Institute of Mass Spectrometry, Ningbo, P.R. China
| | - Jun Zhou
- Zhejiang Ningbo Ecological and Environmental Monitoring Center, Ningbo, P.R. China.
| | - Jiancheng Yu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, P. R. China.
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
- Ningbo Zhenhai Institute of Mass Spectrometry, Ningbo, P.R. China
| | - Keqi Tang
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo, P. R. China.
- Ningbo Zhenhai Institute of Mass Spectrometry, Ningbo, P.R. China
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Zhang W, Cai J, Gao W, Han R, Wang H, Wu Y, Wu J, Wu Y, Wang C, Tang K, Yu J. Overlapping peak separation algorithm for ion mobility spectra based on multistrategy JAYA. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9603. [PMID: 37580846 DOI: 10.1002/rcm.9603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 06/30/2023] [Accepted: 07/02/2023] [Indexed: 08/16/2023]
Abstract
RATIONALE In the field of separation science, ion mobility spectrometry (IMS) plays an important role as an analytical tool. However, the lack of sufficient structural resolution is a common problem in qualitative and quantitative analysis using IMS. A method is needed to solve the problem of overlapping peaks caused by insufficient resolution. METHODS The method uses multiple strategies to more effectively use population information to balance exploration and exploitation capabilities, prevent local optimization, accurately resolve overlapping peaks, quickly obtain optimal spectral peak model coefficients, and accurately identify compounds. RESULTS Multistrategy JAYA algorithm's (MSJAYA) performance is compared with improved particle swarm optimization (IPSO), dynamic inertia weight particle swarm optimization (DIWPSO), and multiobjective dynamic teaching-learning-based optimization (MDTLBO). The analysis shows that MSJAYA's maximum separation error is within 0.6%, a level of accuracy not guaranteed by the other algorithms. In addition, the separation error fluctuates within a much smaller range, demonstrating MSJAYA's superior robustness. CONCLUSIONS Compared with other overlapping peak separation algorithms, MSJAYA is more applicable because no special parameters are used. The method allows fast deconvolution analysis of strong overlapping peaks with multiple components, which greatly improves the resolution of IMS.
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Affiliation(s)
- Weiyang Zhang
- Faculty of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo, China
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Jing Cai
- Academic Affairs Department, Zhejiang Police College, Hangzhou, China
| | - Wenqing Gao
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Renlu Han
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Haixing Wang
- Key Laboratory of Drug Monitoring and Control of Zhejiang Province, National Anti-Drug Laboratory Zhejiang Regional Center, Hangzhou, China
| | - Yanfei Wu
- Key Laboratory of Drug Monitoring and Control of Zhejiang Province, National Anti-Drug Laboratory Zhejiang Regional Center, Hangzhou, China
| | - Jiawei Wu
- Key Laboratory of Drug Monitoring and Control of Zhejiang Province, National Anti-Drug Laboratory Zhejiang Regional Center, Hangzhou, China
| | - Yong Wu
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Chenlu Wang
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Keqi Tang
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Jiancheng Yu
- Faculty of Electrical Engineering and Computer Sciences, Ningbo University, Ningbo, China
- Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Institute of Mass Spectrometry, Ningbo University, Ningbo, China
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Ji X, Liu R, Hao J, Wang C, Li J, Gao W, Yu J, Tang K. Two-step particle swarm optimization algorithm for effective deconvolution and resolution enhancement of various overlapping peaks. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9429. [PMID: 36346291 DOI: 10.1002/rcm.9429] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/30/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
RATIONALE The existing particle swarm optimization (PSO) algorithms are only effective in deconvoluting the overlapping peaks in ion mobility spectra with fewer than four component peaks, which limits the applicability of these algorithms. METHODS A high-performance two-step particle swarm optimization (TSPSO) algorithm was developed. Compared to the existing PSO algorithms, TSPSO can narrow the search ranges of all coefficients for the overlapping peaks through Gaussian model calculation, and thus can deconvolute various overlapping peaks with high accuracy, even for 30-component overlapping peaks. In addition, the TSPSO could be further applied to enhance the resolution of the spectra by narrowing the peak widths after the peak deconvolution. RESULTS Simulated overlapping peaks were first used to evaluate the performance of TSPSO as compared to the dynamic inertia weight particle swarm optimization (DIWPSO) algorithm. The results showed that the profiles of the peaks deconvoluted by using TSPSO were more consistent with the original ones. The fitness values and the standard deviations of the fitness values from TSPSO were also at least an order of magnitude less than those from DIWPSO. By applying TSPSO, the overlapping peaks from both mass spectrometry (MS) and field asymmetric waveform ion mobility spectrometry (FAIMS) spectra can also be well deconvoluted. In addition, the resolutions of the MS and FAIMS spectra can be effectively enhanced after peak deconvolution. The enhanced spectra matched excellently with the experimental ones acquired at high-resolution modes. CONCLUSIONS The experiment results convincingly demonstrate that the TSPSO algorithm is capable of both deconvoluting complex overlapping peaks and enhancing the spectrum resolution with high accuracy.
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Affiliation(s)
- Xiaoli Ji
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Rong Liu
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Jie Hao
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
| | - Chenlu Wang
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Junhui Li
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
| | - Wenqing Gao
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
| | - Jiancheng Yu
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China
| | - Keqi Tang
- Institute of Mass Spectrometry, Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application, Ningbo University, Ningbo, China
- School of Material Science and Chemical Engineering, Ningbo University, Ningbo, China
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Tang X, Yu J, Xie Z, Tang K, Hu S, Li J, Wu Y. Deconvolution of overlapping peaks in ion mobility spectrometry based on a multiobjective dynamic teaching-learning-based optimization. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9379. [PMID: 35986906 DOI: 10.1002/rcm.9379] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/18/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
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.
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Affiliation(s)
- Xu Tang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China
- Ningbo Banff Biotech Inc., Ningbo, China
| | - Jiangcheng Yu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China
- Ningbo Banff Biotech Inc., Ningbo, China
- Institute of Mass Spectrometry, Ningbo University, Ningbo, China
- Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application, Zhejiang Province, China
| | - Zhijun Xie
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China
- Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application, Zhejiang Province, China
| | - Keqi Tang
- Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Shifu Hu
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China
| | - Jun Li
- Ningbo Banff Biotech Inc., Ningbo, China
- Institute of Mass Spectrometry, Ningbo University, Ningbo, China
| | - Yong Wu
- Institute of Mass Spectrometry, Ningbo University, Ningbo, China
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A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction. JOURNAL OF ENVIRONMENTAL AND PUBLIC HEALTH 2022; 2022:5268887. [PMID: 35655949 PMCID: PMC9152402 DOI: 10.1155/2022/5268887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/06/2022] [Indexed: 12/03/2022]
Abstract
Aiming at the problem that particles cannot realize multidimensional analysis and poor global search ability, a composite particle swarm optimization algorithm is proposed, improving the accuracy of particle swarm optimization. Firstly, k-clustering is used to cluster risk management particle swarm optimization. The advantages of particle swarm optimization have to be given full play, and the risk of hospital equipment management from various aspects has to be controlled. Then, the multidimensional particle swarm is segmented to obtain an ordered multidimensional risk particle swarm set, which provides a basis for later risk prediction. Finally, through the fusion function of multidimensional risk particle swarm, the risk particle swarm set based on the clustering degree is constructed, and the optimal extreme value is obtained, so as to improve the accuracy of management risk calculation results. Through MATLAB simulation analysis, it can be seen that the composite particle swarm optimization algorithm is better than particle swarm optimization algorithm in global search accuracy and search time. Moreover, the calculation time and accuracy are better. Therefore, the composite particle swarm optimization algorithm can be used to analyze the risk of hospital equipment and effectively control the risk of hospital equipment management.
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