<?xml version="1.0" encoding="utf-8"?>
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  <doc>
    <id>44847</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3349</pageFirst>
    <pageLast>3360</pageLast>
    <pageNumber/>
    <edition/>
    <issue>14</issue>
    <volume>410</volume>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin, Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Online low-field NMR spectroscopy for process control of an industrial lithiation reaction—automated data analysis</title>
    <abstract language="eng">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 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.&#13;
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – 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 analysed 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.</abstract>
    <parentTitle language="eng">Analytical and Bioanalytical Chemistry</parentTitle>
    <identifier type="doi">10.1007/s00216-018-1020-z</identifier>
    <identifier type="url">https://link.springer.com/article/10.1007/s00216-018-1020-z</identifier>
    <identifier type="issn">1618-2642</identifier>
    <identifier type="issn">1618-2650</identifier>
    <enrichment key="date_peer_review">07.05.2018</enrichment>
    <author>Simon Kern</author>
    <author>Klas Meyer</author>
    <author>Svetlana Guhl</author>
    <author>Patrick Gräßer</author>
    <author>Andrea Paul</author>
    <author>R. King</author>
    <author>Michael Maiwald</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Online NMR spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Process analytical technology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Partial Least Squares Regression</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Indirect Hard Modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchtop NMR Spectroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Smart Sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CONSENS</value>
    </subject>
    <collection role="ddc" number="543">Analytische Chemie</collection>
    <collection role="literaturgattung" number="">Verlagsliteratur</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
  </doc>
</export-example>
