1 / 30

Fermentation monitoring using in-line and non-invasive spectrometric measurements

Fermentation monitoring using in-line and non-invasive spectrometric measurements. C.A. McGill, A. Nordon and D. Littlejohn Dept of Pure and Applied Chemistry/CPACT, University of Strathclyde, Glasgow, UK. Fermentation process. Streptomyces process

nasia
Télécharger la présentation

Fermentation monitoring using in-line and non-invasive spectrometric measurements

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Fermentation monitoring using in-line and non-invasive spectrometric measurements C.A. McGill, A. Nordon and D. Littlejohn Dept of Pure and Applied Chemistry/CPACT, University of Strathclyde, Glasgow, UK

  2. Fermentation process • Streptomyces process • Starting ingredients are carbohydrate, soya protein, oil and trace elements in water • Biomass grown in seed stage then transferred to final stage for production of desired product • Final stage is a fed batch process

  3. Fermentation Process Seed stage - grow biomass Final stage – production of pharmaceutical Transfer biomass Media batching tank

  4. Data currently acquired • On-line (Aspen) • CO2, O2, N2, Ar, pH*, dO2*, RPM*, temp • Off-line • titres*, pH, viscosity • Biochemical analysis • Soluble PO4, CHOs, NH3, proteins, lipids, urea * - final stage only

  5. Aims • Seed stage • optimal transfer time • Final stage • replace off-line measurements • fingerprint batch

  6. Instruments used • Zeiss Corona NIR • Spectraprobe Linx 5-10 MIR

  7. Zeiss Corona NIR

  8. Specification of Zeiss Corona NIR • Reflectance measurements in the 950 – 1700 nm range (6 nm resolution) • Diode array detector • Focal length: 13 mm • 15 detection fibres

  9. SpectraProbe Linx 5-10 MIR Process instrument Lab instrument

  10. Specification of SpectraProbe Linx 5-10 MIR • Probe and spectrometer integrated into a single unit • Hastelloy probe with silicon ATR crystal • 128 element pyro-electric array detector • Chalcogenide fibres transport light to and from sample

  11. Experimental • Seed stage • approx. 50 hours • spectral measurements every 15 min • Final stage • approx. 140 hours • spectral measurements every 15 min

  12. Zeiss Corona monitoring seed stage

  13. SpectraProbe Linx Monitoring the Final Stage

  14. Seed stage • Currently CO2 evolution rate (CER) is used to determine transfer time • This is used for historic reasons – may not be the best time for transfer • Can a spectroscopic technique be used to determine the transfer time?

  15. Seed stage – MIR data Time (possibly C-H bend)

  16. Seed stage – 1st derivative MIR data Time

  17. Seed stage - CER Transfer time

  18. Seed stage – PC1 scores of MIR data Data range: 1400 – 1550 cm-1

  19. Seed stage – MIR results • Small changes in absorbance at ~ 1500 cm-1 • Need to investigate what this change in absorbance means • Can this information be used to determine the optimum time for transferring biomass?

  20. Experimental – final stage • Zeiss Corona NIR • Used NIR data and titre values to construct PLS models for predicting titre • Used NIR data and lipid values to construct PLS models for predicting lipid conc. • Models constructed from data from 6 runs

  21. NIR spectra Time

  22. 1st derivative NIR spectra Time (C-H stretch 2nd overtone) Time (C-H stretch 1st overtone)

  23. Prediction of titre

  24. Prediction of titre

  25. Prediction of Titre Run 1

  26. Prediction of lipids

  27. Prediction of Lipids Run 4

  28. Final Stage - Results • Correlation between NIR data and desired product concentration and lipids concentration • More work required to see if these are direct or indirect correlations • Possible correlations with soluble PO4 and NH3

  29. Overall conclusions • Can possibly use non-invasive NIR instead of off-line analysis to determine titre values or lipids concentration • In-line MIR analysis of the seed stage yields changes in absorbance – needs further investigation

  30. Acknowledgements • GSK Worthing • Barry Barton, Paul Jeffkins, Sarah Stimpson • SpectraProbe Ltd., Clairet Scientific Ltd. • CPACT

More Related