Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-539412
Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method
- Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally 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 realized 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-the-art analysis. These results suggest that, inMass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally 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 realized 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-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a 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.…
Autor*innen: | F. Fricke, M. Brandalero, Sascha LiehrORCiD, Simon KernORCiD, Klas MeyerORCiD, Stefan Kowarik, R. Hierzegger, S. Westerdick, Michael MaiwaldORCiD, M. Hübner |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Transactions on Emerging Topics in Computing |
Jahr der Erstveröffentlichung: | 2021 |
Organisationseinheit der BAM: | 1 Analytische Chemie; Referenzmaterialien |
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik | |
8 Zerstörungsfreie Prüfung | |
8 Zerstörungsfreie Prüfung / 8.0 Abteilungsleitung und andere | |
8 Zerstörungsfreie Prüfung / 8.6 Faseroptische Sensorik | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Verlag: | IEEE |
Jahrgang/Band: | 10 |
Ausgabe/Heft: | 1 |
Erste Seite: | 87 |
Letzte Seite: | 98 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Artificial Neural Networks; Cyber-Physical Systems; Industry 4.0; Mass Spectrometry; Modular Production; Nuclear Magnetic Resonance Spectroscopy |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik | |
Umwelt | |
Umwelt / Sensorik | |
DOI: | 10.1109/TETC.2021.3131371 |
URN: | urn:nbn:de:kobv:b43-539412 |
URL: | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 |
ISSN: | 2168-6750 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 08.12.2021 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 28.03.2022 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |