Applied Biochemistry and Biotechnology

, Volume 102, Issue 1–6, pp 49–62 | Cite as

Fermentation diagnosis by multivariate statistical analysis

  • Silvio Bicciato
  • Andrea Bagno
  • Marco Soldà
  • Riccardo Manfredini
  • Carlo Di Bello
Article

Abstract

During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).

Index Entries

Fermentation processes process identification process diagnosis multiway principal component analysis statistical process control database mining 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Royce, P. N. (1993), Crit. Rev. Biotechnol. 13(2), 117–149.Google Scholar
  2. 2.
    Lübbert, A. and Simutis, R. (1994), TIBTECH 12, 304–311.Google Scholar
  3. 3.
    Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, MA.Google Scholar
  4. 4.
    Stephanopoulos, G. N., Locher, G., Duff, M. J., Kamimura, R. T., and Stephanopoulos, G. (1997), Biotechnol. Bioeng. 53(5), 443–452.CrossRefGoogle Scholar
  5. 5.
    Kamimura, R. T., Bicciato, S., Shimizu, H., Alford, J., and Stephanopoulos, G. N. (2000), Metab. Eng. 2(3), 218–227.PubMedCrossRefGoogle Scholar
  6. 6.
    Nomikos, P. and MacGregor, J. F. (1994), AIChE J. 40(8), 1361–1375.CrossRefGoogle Scholar
  7. 7.
    Nomikos, P. and MacGregor, J. F. (1995), Technometrics 37(1), 41–59.MATHCrossRefGoogle Scholar
  8. 8.
    Glassey, J., Montague, G., and Mohan, P. (2000), TIBTECH 18, 136–141.Google Scholar
  9. 9.
    Jolliffe, I. (1986), Principal Components Analysis, Springer-Verlag, New York.Google Scholar
  10. 10.
    Brereton, R. G. (1992), Multivariate Pattern Recognition in Chemometrics, illustrated by Case Studies, Elsevier, New York.Google Scholar
  11. 12.
    Dillon, W. R. and Goldstein, M. (1984), Multivariate Analysis: Methods and Applications, John Wiley & Sons, New York.MATHGoogle Scholar
  12. 13.
    Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979), Multivariate Analysis, Academic, San Diego.MATHGoogle Scholar
  13. 14.
    Cios, K., Pedrycz, W., and Swiniarski, R. (1998), Data Mining—Methods for Knowledge Discovery, Kluwer Academic, Boston.MATHGoogle Scholar
  14. 15.
    Kamimura, R. T., Bicciato, S., Shimizu, H., Alford, J., and Stephanopoulos, G. N. (2000), Metab. Eng. 2(3), 228–238.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2002

Authors and Affiliations

  • Silvio Bicciato
    • 1
  • Andrea Bagno
    • 1
  • Marco Soldà
    • 1
  • Riccardo Manfredini
    • 2
  • Carlo Di Bello
    • 1
  1. 1.University of PadovaPadovaItaly
  2. 2.Biofin Laboratories SrlPorto MantovanoItaly

Personalised recommendations