• Treffer 9 von 10
Zurück zur Trefferliste

Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy

  • Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation ofMass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.zeige mehrzeige weniger

Volltext Dateien herunterladen

  • Fricke_et_al_DATE_21_Artificial_Intelligence_for_MS_and_NMR.pdf
    eng

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar
Metadaten
Autoren/innen:F. Fricke, S. Mahmood, J. Hoffmann, M. Brandalero, Sascha Liehr, Simon Kern, Klas Meyer, Stefan Kowarik, S. Westerdicky, Michael Maiwald, M. Hübner
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Proceedings of the 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik
Herausgeber (Institution):EDAA
Verlag:Research Publishing
Erste Seite:615
Letzte Seite:620
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Artificial Neural Networks; Cyber-Physical Systems; Industry 4.0; Mass Spectrometry; Nuclear Magnetic Resonance Spectroscopy
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Sensorik
Veranstaltung:2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Veranstaltungsort:Online meeting
Beginndatum der Veranstaltung:01.02.2021
Enddatum der Veranstaltung:05.02.2021
URL:http://www.date-conference.com
ISBN:978-3-9819263-5-4
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:01.03.2021
Referierte Publikation:Nein