Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis
- Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least SquaresMonitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies. We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.…
Autor*innen: | Simon KernORCiD, Klas MeyerORCiD, Svetlana Guhl, Patrick Gräßer, Andrea PaulORCiD, R. King, Michael MaiwaldORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Analytical and Bioanalytical Chemistry |
Jahr der Erstveröffentlichung: | 2018 |
Organisationseinheit der BAM: | 1 Analytische Chemie; Referenzmaterialien |
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik | |
Verlag: | Springer |
Verlagsort: | Berlin, Heidelberg |
Jahrgang/Band: | 410 |
Ausgabe/Heft: | 14 |
Erste Seite: | 3349 |
Letzte Seite: | 3360 |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | Benchtop NMR Spectroscopy; CONSENS; Indirect Hard Modeling; Online NMR spectroscopy; Partial Least Squares Regression; Process analytical technology; Smart Sensors |
Themenfelder/Aktivitätsfelder der BAM: | Chemie und Prozesstechnik |
Chemie und Prozesstechnik / Chemische Charakterisierung und Spurenanalytik | |
Umwelt | |
Umwelt / Sensorik | |
DOI: | 10.1007/s00216-018-1020-z |
URL: | https://link.springer.com/article/10.1007/s00216-018-1020-z |
ISSN: | 1618-2642 |
ISSN: | 1618-2650 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 07.05.2018 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 07.05.2018 |