Ji Xiaoli, Liu Rong, Hao Jie, Wang Chenlu, Li Junhui, Gao Wenqing, Yu Jiancheng, Tang Keqi
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.
Rapid Commun Mass Spectrom. 2023 Feb 15;37(3):e9429. doi: 10.1002/rcm.9429.
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.
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.
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.
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.
现有的粒子群优化（PSO）算法仅在反卷积少于四个成分峰的离子迁移谱中的重叠峰时有效，这限制了这些算法的适用性。
开发了一种高性能的两步粒子群优化（TSPSO）算法。与现有的PSO算法相比，TSPSO可以通过高斯模型计算缩小重叠峰所有系数的搜索范围，从而能够高精度地反卷积各种重叠峰，即使对于30个成分的重叠峰也是如此。此外，TSPSO可以进一步应用于在峰反卷积后通过缩小峰宽来提高谱的分辨率。
首先使用模拟的重叠峰来评估TSPSO与动态惯性权重粒子群优化（DIWPSO）算法相比的性能。结果表明，使用TSPSO反卷积的峰轮廓与原始峰更一致。TSPSO的适应度值和适应度值的标准差也比DIWPSO至少小一个数量级。通过应用TSPSO，质谱（MS）和场不对称波形离子迁移谱（FAIMS）谱中的重叠峰也可以得到很好的反卷积。此外，峰反卷积后MS和FAIMS谱的分辨率可以有效提高。增强后的谱与在高分辨率模式下获得的实验谱非常匹配。
实验结果令人信服地证明，TSPSO算法能够高精度地反卷积复杂的重叠峰并提高谱分辨率。
