Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-419498
Reading between the lines – Automated data analysis for low field NMR spectra
- For reaction monitoring and process control using NMR instruments, in particular, after acquisition of the FID the data needs to be corrected in real-time for common effects using fast interfaces and automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. Additionally, the multiplet structure becomes more dominant because of the comparably low-field strengths which results in overlapping of multiple signals. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modeling). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process AnalyticalFor reaction monitoring and process control using NMR instruments, in particular, after acquisition of the FID the data needs to be corrected in real-time for common effects using fast interfaces and automated methods. When it comes to NMR data evaluation under industrial process conditions, the shape of signals can change drastically due to nonlinear effects. Additionally, the multiplet structure becomes more dominant because of the comparably low-field strengths which results in overlapping of multiple signals. However, the structural and quantitative information is still present but needs to be extracted by applying predictive models. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modeling). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants, or derivatives and robust automation schemes.…
Autor*innen: | Simon KernORCiD |
---|---|
Koautor*innen: | Svetlana Guhl, Klas MeyerORCiD, Andrea PaulORCiD, Michael MaiwaldORCiD |
Dokumenttyp: | Posterpräsentation |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2017 |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
DDC-Klassifikation: | Naturwissenschaften und Mathematik / Chemie / Analytische Chemie |
Freie Schlagwörter: | CONSENS; Chemometrics; Data analysis; Indirect hard modeling; Line prediction; Online NMR spectroscopy; SMASH; Spectral modeling |
Veranstaltung: | Small Molecule NMR Conference (SMASH) |
Veranstaltungsort: | Baveno, Italy |
Beginndatum der Veranstaltung: | 17.09.2017 |
Enddatum der Veranstaltung: | 20.09.2017 |
URN: | urn:nbn:de:kobv:b43-419498 |
URL: | http://www.smashnmr.org/conference/program |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung |
Datum der Freischaltung: | 18.09.2017 |
Referierte Publikation: | Nein |
Schriftenreihen ohne Nummerierung: | BAM Präsentationen |