Articles | Volume 25, issue 1
https://doi.org/10.5194/acp-25-685-2025
https://doi.org/10.5194/acp-25-685-2025
Technical note
 | 
17 Jan 2025
Technical note |  | 17 Jan 2025

Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning

Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen

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Cited articles

Besel, V., Todorović, M., Kurtén, T., Rinke, P., and Vehkamäki, H.: Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules, Scientific data, 10, 450, https://doi.org/10.1038/s41597-023-02366-x, 2023.​​​​​​​ a
Besel, V., Todorović, M., Kurtén, T., Vehkamäki, H., and Rinke, P.: The search for sparse data in molecular datasets: Application of active learning to identify extremely low volatile organic compounds, J. Aerosol Sci., 179, 106375, https://doi.org/10.1016/j.jaerosci.2024.106375, 2024. a
Bortolussi, F., Partovi, F., Joona, M., and Matti, R.: Organic pesticide database with 716 molecules analyzed with chemical ionization mass spectrometry. Reagent ions: bromide, protonated acetone, hydronium ion, dioxide, Zenodo [data set], https://doi.org/10.5281/zenodo.11208543, 2024a. a
Bortolussi, F., Sandström, H., and Rinke, P.: PesticidesMS, Gitlab [code], https://gitlab.com/cest-group/pesticidesms (last access: 15 January 2025), 2024b. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001. a, b
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Short summary
Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
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