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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 [1]. 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 [2]). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (ProcessFor 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 [1]. 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 [2]). 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. References: 1. Meyer, K., Kern, S., Zientek, N., Guthausen, G., & Maiwald, M. (2016). Process control with compact NMR. TrAC Trends in Analytical Chemistry, 83, 39-52. 2. Michalik-Onichimowska, A., Kern, S., Riedel, J., Panne, U., King, R., & Maiwald, M.: Journal of Magnetic Resonance (2017), 277, 154-161.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Simon KernORCiD
Koautoren/innen:Svetlana Guhl, Klas Meyer, Andrea Paul, Michael Maiwald
Dokumenttyp:Posterpräsentation
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2017
Organisationseinheit der BAM:1 Analytische Chemie; Referenzmaterialien
1 Analytische Chemie; Referenzmaterialien / 1.4 Prozessanalytik
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
Themenfelder/Aktivitätsfelder der BAM:Analytical Sciences
Analytical Sciences / Sensorik
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 Volltexts:Volltext-PDF für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung
Datum der Freischaltung:18.09.2017
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:BAM Präsentationen