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).…
Autor*innen: | Francesco Friedrich Russo |
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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 |