Skip to main content
SpringerLink
Log in

In-line monitoring of Bordetella pertussis cultivation using fluorescence spectroscopy

  • Research Paper
  • Published:
Bioprocess and Biosystems Engineering Aims and scope Submit manuscript

Abstract

Fluorescence spectroscopy is a non-invasive and highly sensitive method for bioprocess monitoring. The use of fluorescence spectroscopy is not very well established in the industry for in-line monitoring. In the present work, a 2-D fluorometer with two excitation lights (365 and 405 nm) and emission spectra in the range of 350–850 nm were used for in-line monitoring of two strains of Bordetella pertussis cultivation operated in batch and fed batch. A Partial Least Squares (PLS) based regression model was used for the estimation of cell biomass, amino acids (glutamate and proline) and antigen (Pertactin) produced. It was observed that accurate predictions were achieved when models were calibrated separately for each cell strain and nutrient media formulation. Also, prediction accuracy was improved when dissolved oxygen, agitation and culture volume are added as additional features in the regression model. The proposed approach of combining in-line fluorescence and other online measurements is shown to have good potential for in-line monitoring of bioprocesses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

The dataset generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Agarwal P, Tamer M, Budman H (2021) Explainability: relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes. Comput Chem Eng 154:107467

    Article  CAS  Google Scholar 

  2. Agarwal P, Tamer M, Sahraei MH, Budman H (2019) Deep learning for classification of profit-based operating regions in industrial processes. Ind Eng Chem Res 59(6):2378–2395

    Article  Google Scholar 

  3. Mandenius C-F, Titchener-Hooker NJ et al (2013) Measurement, monitoring, modelling and control of bioprocesses, vol 132. Springer

    Book  Google Scholar 

  4. Korshin GV, Li C-W, Benjamin MM (1997) Monitoring the properties of natural organic matter through UV spectroscopy: a consistent theory. Water Res 31(7):1787–1795

    Article  CAS  Google Scholar 

  5. Kara S, Mueller J, Liese A (2011) Online analysis methods for monitoring of bioprocesses. Chim Oggi 29:38–41

    CAS  Google Scholar 

  6. Long DA (1977) Raman spectroscopy. New York 1

  7. Royer CA (1995) Fluorescence spectroscopy. Protein Stabil Fold 65–89

  8. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  CAS  Google Scholar 

  9. Höskuldsson A (1988) Pls regression methods. J Chemometrics 2(3):211–228

    Article  Google Scholar 

  10. Braspenning PJ, Thuijsman F, Weijters AJMM (1995) Artificial neural networks: an introduction to ANN theory and practice, vol 931. Springer Science & Business Media

    Google Scholar 

  11. Yi L et al (2016) Chemometric methods in data processing of mass spectrometry-based metabolomics: a review. Anal Chim Acta 914:17–34

    Article  CAS  PubMed  Google Scholar 

  12. Abbas O, Pissard A, Baeten V (2020) In near-infrared, mid-infrared, and Raman spectroscopy. Elsevier, pp 77–134

    Google Scholar 

  13. Haas J, Mizaikoff B (2016) Advances in mid-infrared spectroscopy for chemical analysis. Annu Rev Anal Chem 9:45–68

    Article  Google Scholar 

  14. Fazenda ML et al (2013) Towards better understanding of an industrial cell factory: investigating the feasibility of real-time metabolic flux analysis in pichia pastoris. Microb Cell Fact 12(1):1–14

    Article  Google Scholar 

  15. Graves P, Gardiner D (1989) Practical Raman spectroscopy. Springer

    Google Scholar 

  16. Shaw AD et al (1999) Noninvasive, on-line monitoring of the biotransformation by yeast of glucose to ethanol using dispersive Raman spectroscopy and chemometrics. Appl Spectrosc 53(11):1419–1428

    Article  CAS  Google Scholar 

  17. Faassen SM, Hitzmann B (2015) Fluorescence spectroscopy and chemometric modeling for bioprocess monitoring. Sensors 15(5):10271–10291

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ödman P, Johansen CL, Olsson L, Gernaey KV, Lantz AE (2010) Sensor combination and chemometric variable selection for online monitoring of streptomyces coelicolor fed-batch cultivations. Appl Microbiol Biotechnol 86(6):1745–1759

    Article  PubMed  Google Scholar 

  19. Teixeira AP et al (2009) In situ 2d fluorometry and chemometric monitoring of mammalian cell cultures. Biotechnol Bioeng 102(4):1098–1106

    Article  CAS  PubMed  Google Scholar 

  20. Teixeira AP, Duarte TM, Carrondo M, Alves PM (2011) Synchronous fluorescence spectroscopy as a novel tool to enable pat applications in bioprocesses. Biotechnol Bioeng 108(8):1852–1861

    Article  CAS  PubMed  Google Scholar 

  21. Graf A, Claßen J, Solle D, Hitzmann B, Rebner K, Hoehse M (2019) A novel led-based 2d-fluorescence spectroscopy system for in-line monitoring of Chinese hamster ovary cell cultivations-part I. Eng Life Sci 19(5):352–362

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Karakach TK, Dachon A, Choi J, Miguez C, Masson M, Tartakovsky B (2019) Fluorescence-based real time monitoring and diagnostics of recombinant Pichia pastoris cultivations in a bioreactor. Biotechnol Prog 35(2):e2761

    Article  PubMed  Google Scholar 

  23. Hu S (2007) Akaike information criterion. Center Res Sci Comput 93:42

  24. Cavanaugh JE, Neath AA (2019) The Akaike information criterion: background, derivation, properties, application, interpretation, and refinements. Wiley Interdisciplinary Rev 11(3):e1460

    Article  Google Scholar 

  25. Proom H, Knight B (1955) The minimal nutritional requirements of some species in the genus bacillus. Microbiology 13(3):474–480

    CAS  Google Scholar 

  26. Blacker TS, Duchen MR (2016) Investigating mitochondrial redox state using nadh and nadph autofluorescence. Free Radical Biol Med 100:53–65

    Article  CAS  Google Scholar 

  27. Ashoori M, Saedisomeolia A (2014) Riboflavin (vitamin b2) and oxidative stress: a review. Br J Nutr 111(11):1985–1991

    Article  CAS  PubMed  Google Scholar 

  28. Wellen KE, Thompson CB (2010) Cellular metabolic stress: considering how cells respond to nutrient excess. Mol Cell 40(2):323–332

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was financially supported by Sanofi and Mitacs (IT6479). We are also immensely thankful to Dr. Boris Tartakovsky (National Research Council, Montreal) for his support.

Author information

Authors and Affiliations

  1. Department of Chemical Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada

    Abhishek Mishra & Hector Budman

  2. Sanofi, Toronto, ON, M2R 3T4, Canada

    Melih Tamer

Authors
  1. Abhishek Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melih Tamer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hector Budman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hector Budman.

Ethics declarations

Conflict of Interest

Melih Tamer is a Sanofi employee and may hold shares and/or stock options in the company.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mishra, A., Tamer, M. & Budman, H. In-line monitoring of Bordetella pertussis cultivation using fluorescence spectroscopy. Bioprocess Biosyst Eng 46, 789–802 (2023). https://doi.org/10.1007/s00449-023-02857-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00449-023-02857-6

Keywords

Search

Navigation