Hu Xiangyang, Zhou Junfei, Li Junhui, Gao Wenqing, Zhou Jun, Yu Jiancheng, Tang Keqi
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.
Analyst. 2023 Oct 23;148(21):5514-5524. doi: 10.1039/d3an01042b.
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.
尽管离子迁移谱（IMS）在聚糖分析中很受欢迎，但其有限的结构分辨率阻碍了许多聚糖异构体的有效分离。这导致IMS峰重叠，从而影响聚糖组成的准确鉴定。为此，提出了一种改进算法，即基于正弦自适应权重的二阶微分结合模拟退火粒子群优化算法（DWSA - PSO），用于分离由聚糖异构体形成的重叠IMS峰。DWSA - PSO首先进行二阶微分，以自动确定重叠峰中的组分数，并排除不可能的单峰组合。然后引入正弦自适应权重和模拟退火机制，以提高算法的搜索能力和全局优化性能，从而实现单个峰的准确高效分离。为了评估DWSA - PSO的性能及其在聚糖异构体分离中的应用，模拟了多组不同重叠程度的重叠峰，并使用六种二糖和四种三糖异构体形成了各种类型的多组分重叠峰。实验结果一致表明，DWSA - PSO算法在分离精度、运行时间和适应度值方面均优于改进粒子群优化（IPSO）算法和动态惯性权重粒子群优化（DIWPSO）算法。此外，DWSA - PSO算法成功应用于麦芽乳饮料中聚糖异构体的分离。所有这些结果都揭示了DWSA - PSO算法有助于准确鉴定聚糖异构体的能力。
