Journal
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 410, Issue 14, Pages 3349-3360Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00216-018-1020-z
Keywords
Online NMR spectroscopy; Process analytical technology; Partial least squares regression; Indirect hard modeling; Benchtop NMR spectroscopy; Smart sensors
Funding
- European Union [636942]
- H2020 Societal Challenges Programme [636942] Funding Source: H2020 Societal Challenges Programme
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available