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Upscaling of spectral libraries for HREI- MS based non target analytics

  • Gas Chromatography coupled with Electron Ionisation Mass Spectrometry (GC-EI-MS) is a well-established technique which, in combination with spectral libraries, has the potential to identify compounds in a sample. Nevertheless, most libraries are dominated by spectra with nominal mass resolution which does not allow to make full use of the data generated by modern high-resolution instruments. The production of high-resolution spectral library is time consuming and expensive, while in silico fragmentation tools that are capable of generating HR mass spectra are still too computationally intensive. We explored the alternative of using ML models to upscale existing spectral libraries. The models were trained with spectra from the RECETOX metabolome (DOI: 10.5281/zenodo.5483564), RECETOX Exposome (DOI: 10.5281/zenodo.4471216), and MassBank (10.5281/zenodo.7436394) HR-GC-MS libraries. The model was used to generate a synthetic library which was compared with a synthetic library simulatedGas Chromatography coupled with Electron Ionisation Mass Spectrometry (GC-EI-MS) is a well-established technique which, in combination with spectral libraries, has the potential to identify compounds in a sample. Nevertheless, most libraries are dominated by spectra with nominal mass resolution which does not allow to make full use of the data generated by modern high-resolution instruments. The production of high-resolution spectral library is time consuming and expensive, while in silico fragmentation tools that are capable of generating HR mass spectra are still too computationally intensive. We explored the alternative of using ML models to upscale existing spectral libraries. The models were trained with spectra from the RECETOX metabolome (DOI: 10.5281/zenodo.5483564), RECETOX Exposome (DOI: 10.5281/zenodo.4471216), and MassBank (10.5281/zenodo.7436394) HR-GC-MS libraries. The model was used to generate a synthetic library which was compared with a synthetic library simulated with CFM-ID (DOI: 10.1021/acs.analchem.6b01622).zeige mehrzeige weniger

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Metadaten
Autor*innen:Francesco Friedrich Russo
Koautor*innen:Jan LisecORCiD
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2023
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.7 Organische Spuren- und Lebensmittelanalytik
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:EI-MS; GC-MS; ML
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
Veranstaltung:ANAKON 2023
Veranstaltungsort:Vienna, Austria
Beginndatum der Veranstaltung:11.04.2023
Enddatum der Veranstaltung:14.04.2023
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:21.04.2023
Referierte Publikation:Nein
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