Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang, China.
Ningbo Banff Biotech Inc., Ningbo, China.
Rapid Commun Mass Spectrom. 2023 Jan 15;37(1):e9379. doi: 10.1002/rcm.9379.
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.
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.
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.
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.
由于其强大的分析能力，离子淌度谱（IMS）在质谱领域中发挥着重要作用。然而，IMS 的主要缺陷之一是其结构分辨率低，这导致在分析具有相似质荷比的化合物时出现峰重叠现象。
提出了一种多目标动态教学优化（MDTLBO）方法来分离 IMS 重叠峰。该方法可防止局部优化并有效地识别峰模型系数。此外，粒子的位置信息在很大程度上反映了 IMS 的半峰宽，这使得单峰难以出现，系数识别更容易。
MDTLBO 与其他解卷积方法（遗传算法、改进粒子群优化算法和动态惯性权重粒子群优化算法）的性能比较表明，MDTLBO 的最大解卷积误差仅为 0.7%，远低于其他三种方法。此外，稳健性是反映算法优缺点的性能指标。
MBTLBO 比其他算法更能稳健地分离重叠峰。该算法可以分离严重重叠的淌度峰，产生更好的分析结果，并提高 IMS 的分辨率。
