Articles | Volume 17, issue 1
https://doi.org/10.5194/amt-17-299-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-299-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jan 2024
Research article |
| 16 Jan 2024
| 16 Jan 2024
Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Julian Schade
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Johannes Passig
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
Thomas Adam
Department of Mechanical Engineering, Institute of Chemistry and Environmental Engineering, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Günther Dollinger
Department of Aerospace Engineering, Institute for Applied Physics and Measurement Technology, University of the Bundeswehr Munich, 85577 Neubiberg, Germany
Ralf Zimmermann
Joint Mass Spectrometry Centre, Institute of Chemistry, Division of Analytical and Technical Chemistry, University of Rostock, 18059 Rostock, Germany
Joint Mass Spectrometry Centre, Helmholtz Zentrum München, 85764 Neuherberg, Germany
Department of Life, Light and Matter, Faculty of Interdisciplinary Faculty, University of Rostock, 18059 Rostock, Germany
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- Real-Time Detection of Urban Atmospheric Micro–Nanoplastics and Their Chemical Mixing State Using Bioaerosol Single-Particle Mass Spectrometry C. Zhang et al. 10.1021/acs.est.5c06513
- Deep learning-based analysis and identification of single-particle mass spectra of bacteria H. Chen et al. 10.1007/s00216-025-05942-9
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8 citations as recorded by crossref.
- A new aggregation and riming discrimination algorithm based on polarimetric weather radars A. Blanke et al. 10.5194/acp-25-4167-2025
- Molecular characterization of atmospheric organic aerosols: Contemporary applications of high-resolution mass spectrometry Q. Xie & A. Laskin 10.1016/j.trac.2024.117986
- Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning Y. Xu et al. 10.1016/j.scitotenv.2024.172822
- Selective capture of PM2.5 by urban trees: The role of leaf wax composition and physiological traits in air quality enhancement D. Chen et al. 10.1016/j.jhazmat.2024.135428
- Self-organizing maps for the detection and classification of natural nanoparticles, nanoparticle systems and engineered nanoparticles characterized using single particle ICP-time-of-flight-MS C. Cuss et al. 10.1039/D5JA00179J
- Deep learning based aerosol particle classification for the detection of ship emissions G. Wang et al. 10.1016/j.scitotenv.2025.180041
- Advanced Characterization of Industrial Smoke: Particle Composition and Size Analysis with Single Particle Aerosol Mass Spectrometry and Optimized Machine Learning Y. Ye et al. 10.1021/acs.analchem.4c05988
- Real-Time Detection of Urban Atmospheric Micro–Nanoplastics and Their Chemical Mixing State Using Bioaerosol Single-Particle Mass Spectrometry C. Zhang et al. 10.1021/acs.est.5c06513
3 citations as recorded by crossref.
- Deep learning-based analysis and identification of single-particle mass spectra of bacteria H. Chen et al. 10.1007/s00216-025-05942-9
- Machine learning approaches for automatic classification of single-particle mass spectrometry data G. Wang et al. 10.5194/amt-17-299-2024
- 1D-CNN Network Based Real-Time Aerosol Particle Classification With Single-Particle Mass Spectrometry G. Wang et al. 10.1109/LSENS.2023.3315554
Latest update: 14 Oct 2025
Short summary
This research aims to develop a novel warning system for the real-time monitoring of pollutants in the atmosphere. The system is capable of sampling and investigating airborne aerosol particles on-site, utilizing artificial intelligence to learn their chemical signatures and to classify them in real time. We applied single-particle mass spectrometry for analyzing the chemical composition of aerosol particles and suggest several supervised algorithms for highly reliable automatic classification.
This research aims to develop a novel warning system for the real-time monitoring of pollutants...
