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Cole–Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass

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Abstract

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole–Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole–Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole–Cole and PLS models, the latter technique giving more satisfactory results.

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Acknowledgments

The Swiss National Science Foundation is greatly acknowledged for financial support of this work. Special thanks to Jonas Schenk for help with the LabView interface and data acquisition systems.

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Authors and Affiliations

  1. Laboratory of Chemical and Biological Engineering (LGCB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Station 6, 1015, Lausanne, Switzerland

    Michal Dabros, Danielle Dennewald & Urs von Stockar

  2. Department of Computer Science, University of Wales, Aberystwyth, UK

    David J. Currie & Mark H. Lee

  3. School of Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland

    Ian W. Marison

  4. Aber Instruments Ltd, Science Park, Aberystwyth, UK

    Robert W. Todd

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  1. Michal Dabros
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  2. Danielle Dennewald
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  3. David J. Currie
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  4. Mark H. Lee
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  5. Robert W. Todd
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  6. Ian W. Marison
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  7. Urs von Stockar
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Correspondence to Urs von Stockar.

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Dabros, M., Dennewald, D., Currie, D.J. et al. Cole–Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass. Bioprocess Biosyst Eng 32, 161–173 (2009). https://doi.org/10.1007/s00449-008-0234-4

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  • DOI: https://doi.org/10.1007/s00449-008-0234-4

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