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Volume 7, issue 2 | Copyright

Special issue: Dresden Sensor Symposium 2017

J. Sens. Sens. Syst., 7, 461-467, 2018
https://doi.org/10.5194/jsss-7-461-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Regular research article 08 Aug 2018

Regular research article | 08 Aug 2018

Development and characterisation of a new fluorescence sensor for online monitoring of bioprocesses

Jan C. König1, Tobias Steinwedel1, Dörte Solle1, Patrick Lindner1, Ingo de Vries1, Thorleif Hentrop1, Michael Findeis2, Gernot T. John2, Thomas Scheper1, and Sascha Beutel1 Jan C. König et al.
  • 1Institute of Technical Chemistry, Leibniz University of Hanover, 30167 Hanover, Germany
  • 2PreSenS GmbH, 93053 Regensburg, Germany

Abstract. Fluorescence spectroscopy is a highly sensitive and non-invasive technique for the identification of characteristic process states and for the online monitoring of substrate and product concentrations. Nevertheless, fluorescence sensors are mainly used in academic studies and are not well implemented for monitoring of industrial production processes. In this work, we present a newly developed robust online fluorescence sensor that facilitates the analysis of fluorescence measurements. The set-up of the sensor was miniaturised and realised without any moveable part to be robust enough for application in technical environments. It was constructed to measure only the three most important biologic fluorophores (tryptophan, NADH and FAD/FMN), resulting in a significant data reduction compared to conventional a 2-D fluorescence spectrometer. The sensor performance was evaluated by calibration curves and selectivity tests. The measuring ranges were determined as 0.5–50µmol L−1 for NADH and 0.0025–7.5µmol L−1 for BSA and riboflavin. Online monitoring of batch cultivations of wild-type Escherichia coli K1 in a 10L bioreactor scale were performed. The data sets were analysed using principal component analysis and partial least square regression. The recorded fluorescence data were successfully used to predict the biomass of an independent cultivation (RMSEP 4.6%).

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Fluorescence spectroscopy represents a very sensitive method, but fluorescence sensors are mainly used in academia and rarely in industrial processes. In this work, we present a newly developed functional model of an online fluorescence sensor. The sensor was miniaturised and constructed without any movable parts to detect important biologic fluorophores. Its performance was evaluated by calibration curves and selectivity tests and it was used for a biomass prediction of E. coli cultivations.
Fluorescence spectroscopy represents a very sensitive method, but fluorescence sensors are...
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