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231224s2016 xx |||||o 00| ||eng c |
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|a 10.1002/mrc.4216
|2 doi
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|a pubmed24n0826.xml
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|a (DE-627)NLM247866652
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|a (NLM)25854892
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Zientek, Nicolai
|e verfasserin
|4 aut
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| 245 |
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|a Automated data evaluation and modelling of simultaneous (19) F-(1) H medium-resolution NMR spectra for online reaction monitoring
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|c 2016
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 24.01.2018
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|a Date Revised 24.01.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright © 2015 John Wiley & Sons, Ltd.
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|a Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and process monitoring. In contrast to high-resolution online NMR (HR-NMR), MR-NMR can be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture from the reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by (1) H HR-NMR (500 MHz) and (1) H and (19) F MR-NMR (43 MHz) as a model system. The parallel online measurement is realised by splitting the flow, which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for (1) H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment. Copyright © 2015 John Wiley & Sons, Ltd
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|a Journal Article
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|a 19F
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|a 1H
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|a IHM
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|a NMR
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|a PLS
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|a automation
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|a data processing
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|a medium-resolution NMR
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|a online NMR
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| 650 |
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|a process analytical technology
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| 650 |
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4 |
|a quantitative NMR
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| 650 |
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4 |
|a reaction monitoring
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| 700 |
1 |
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|a Laurain, Clément
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Meyer, Klas
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Paul, Andrea
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Engel, Dirk
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guthausen, Gisela
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kraume, Matthias
|e verfasserin
|4 aut
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| 700 |
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|a Maiwald, Michael
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Magnetic resonance in chemistry : MRC
|d 1985
|g 54(2016), 6 vom: 07. Juni, Seite 513-20
|w (DE-627)NLM098179667
|x 1097-458X
|7 nnns
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| 773 |
1 |
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|g volume:54
|g year:2016
|g number:6
|g day:07
|g month:06
|g pages:513-20
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|u http://dx.doi.org/10.1002/mrc.4216
|3 Volltext
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|d 54
|j 2016
|e 6
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|h 513-20
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