Analytical and Bioanalytical Chemistry

, Volume 410, Issue 14, pp 3349–3360 | Cite as

Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis

  • Simon Kern
  • Klas Meyer
  • Svetlana Guhl
  • Patrick Gräßer
  • Andrea Paul
  • Rudibert King
  • Michael Maiwald
Research Paper


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 enabling 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 modeling—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 analyzed 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.

Graphical abstract

NMR sensor module for monitoring of the aromatic coupling of 1-fluoro-2-nitrobenzene (FNB) with aniline to 2-nitrodiphenylamine (NDPA) using lithium-bis(trimethylsilyl) amide (Li-HMDS) in continuous operation. Online 43.5 MHz low-field NMR (LF) was compared to 500 MHz high-field NMR spectroscopy (HF) as reference method


Online NMR spectroscopy Process analytical technology Partial least squares regression Indirect hard modeling Benchtop NMR spectroscopy Smart sensors 



The authors thank Lukas Wander for his help plotting Fig. 3.

Funding information

This study was supported by the funding of CONSENS by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 636942.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

216_2018_1020_MOESM1_ESM.pdf (977 kb)
ESM 1 (PDF 976 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Simon Kern
    • 1
    • 2
  • Klas Meyer
    • 1
  • Svetlana Guhl
    • 1
  • Patrick Gräßer
    • 1
  • Andrea Paul
    • 1
  • Rudibert King
    • 2
  • Michael Maiwald
    • 1
  1. 1.Division Process Analytical TechnologyBundesanstalt für Materialforschung und -prüfung (BAM)BerlinGermany
  2. 2.Department Measurement and Control, Institute of Process EngineeringBerlin University of TechnologyBerlinGermany

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