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Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

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Abstract

The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic β-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling’s T 2 was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.

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Acknowledgments

The authors thank the Brazilian research-funding agencies FAPESP (State of São Paulo), CNPq and FINEP (Federal).

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Authors and Affiliations

  1. Departamento de Engenharia Química, Universidade Federal de São Carlos, P.O. Box 676, São Carlos, SP, 13565-905, Brazil

    Edson Romano Nucci, Antonio J. G. Cruz & Roberto C. Giordano

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  1. Edson Romano Nucci
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  2. Antonio J. G. Cruz
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  3. Roberto C. Giordano
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Correspondence to Roberto C. Giordano.

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Nucci, E.R., Cruz, A.J.G. & Giordano, R.C. Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study. Bioprocess Biosyst Eng 33, 557–564 (2010). https://doi.org/10.1007/s00449-009-0377-y

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  • DOI: https://doi.org/10.1007/s00449-009-0377-y

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