化学
粒子群优化
离子迁移光谱法
径向基函数
粒子(生态学)
离子
功能(生物学)
基础(线性代数)
质谱法
算法
惯性参考系
人工神经网络
生物系统
色谱法
物理
人工智能
经典力学
计算机科学
进化生物学
生物
海洋学
地质学
有机化学
数学
几何学
作者
Binxin Shou,Mingguang Yang,Zhixin Song,Junhui Li,Wenqing Gao,Wenqing Gao,Jingyi Feng,Jiancheng Yu
摘要
Rationale Ion mobility spectrometry (IMS), as a promising analytical tool, has been widely employed in the structural characterization of biomolecules. Nevertheless, the inherent limitation in the structural resolution of IMS frequently results in peak overlap during the analysis of isomers exhibiting comparable structures. Methods The radial basis function (RBF) neural network optimization algorithm based on dynamic inertial weight particle swarm optimization (DIWPSO) was proposed for separating overlapping peaks in IMS. The RBF network structure and parameters were optimized using the DIWPSO algorithm. By extensively training using a large dataset, an adaptive model was developed to effectively separate overlapping peaks in IMS data. This approach successfully overcomes issues related to local optima, ensuring efficient and precise separation of overlapping peaks. Results The method's performance was evaluated using experimental validation and analysis of overlapping peaks in the IMS spectra of two sets of isomers: 3′/6′‐sialyllactose; fructose‐6‐phosphate, glucose‐1‐phosphate, and glucose‐6‐phosphate. A comparative analysis was conducted using other algorithms, including the sparrow search algorithm, DIWPSO algorithm, and multi‐objective dynamic teaching‐learning‐based optimization algorithm. The comparison results show that the DIWPSO‐RBF algorithm achieved remarkably low maximum relative errors of only 0.42%, 0.092%, and 0.41% for ion height, mobility, and half peak width, respectively. These error rates are significantly lower than those obtained using the other three algorithms. Conclusions The experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS.
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