Affiliation:
1. Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo P. R. China
2. 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
3. Ningbo Zhenhai Institute of Mass Spectrometry Ningbo P. R. China
4. Zhejiang Ningbo Ecological and Environmental Monitoring Center Ningbo P. R. China
Abstract
RationaleIon 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.MethodsThe 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.ResultsThe 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.ConclusionsThe experimental results convincingly demonstrate that this method can adaptively, rapidly, and accurately separate overlapping peaks of multiple components, improving the structural resolution of IMS.
Funder
National Natural Science Foundation of China
Key Research and Development Program of Zhejiang Province
Natural Science Foundation of Zhejiang Province
Science and Technology Innovation 2025 Major Project of Ningbo
Subject
Organic Chemistry,Spectroscopy,Analytical Chemistry