<div class="csl-bib-body">
<div class="csl-entry">Kastenhofer, J., Libiseller-Egger, J., Rajamanickam, V., & Spadiut, O. (2021). Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats. <i>Processes</i>, <i>9</i>(3), 1–17. https://doi.org/10.3390/pr9030422</div>
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dc.identifier.issn
2227-9717
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/20664
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dc.description.abstract
During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance.
en
dc.language.iso
en
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dc.publisher
MDPI
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dc.relation.ispartof
Processes
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
ATR-FTIR spectroscopy
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dc.subject
bioprocess monitoring
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dc.subject
chemometrics
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dc.subject
machine learning
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dc.subject
process analytical technology
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dc.subject
quality by design
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dc.title
Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats