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Online monitoring of a bioprocess based on a multi-analyser system and multivariate statistical process modelling

Authors

  • Christian Cimander,

    1. Division of Biotechnology, Department of Physics and Measurement Technology, Linköping University, 58183 Linköping, Sweden
    Current affiliation:
    1. Novozymes Biopharma AB, Box 965, 22009 Lund, Sweden
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  • Carl-Fredrik Mandenius

    Corresponding author
    1. Division of Biotechnology, Department of Physics and Measurement Technology, Linköping University, 58183 Linköping, Sweden
    • Division of Biotechnology, Department of Physics and Measurement Technology, Linköping University, 58183 Linköping, Sweden
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Abstract

Multivariate statistical process control (MSPC) was for the first time applied to analyse data from a bioprocess on-line multi-analyser system consisting of an electronic nose (EN), a near-infrared spectroscope (NIRS), a mass spectrometer (MS) and standard bioreactor probes. One hundred and fifty sensor signals from the electronic nose, 1050 wavelength signals from the NIRS, carbon dioxide evolution rate calculated from mass spectrometer signals and standard bioreactor data (eg amount of substrate fed) were interrogated for their ability to model a bioprocess using MSPC. The models obtained were validated on a recombinant Escherichia coli fed-batch process for tryptophan production. Limiting trajectories were defined in the MSPC models for warning, action, and process experience with respect to biomass and tryptophan concentrations. The results showed the capacity and robustness of MSPC models for monitoring with multi-analysers and allowed a comparison of the different analysers' suitability for this kind of data processing. Furthermore, the results demonstrate that MSPC models provide a functional and versatile framework for coping with large information flows and are also suited to a variety of other bioprocessing monitoring and control tasks.

© 2002 Society of Chemical Industry

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