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Related Experiment Videos

Monitoring a complex medium fermentation with sample-sample two-dimensional FT-NIR correlation spectroscopy.

Ana P Ferreira1, José C Menezes

  • 1Centre for Biological and Chemical Engineering, IST, Technical University of Lisbon, Av. Rovisco Pais, P-1049-001 Lisbon, Portugal.

Biotechnology Progress
|June 3, 2006
PubMed
Summary

Near-infrared (NIR) spectroscopy combined with sample-sample two-dimensional correlation spectroscopy (SS-2DCoS) effectively monitors fermentation state transitions. This method identifies microbial metabolic and morphological changes during the process.

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Process Monitoring

Background:

  • Fermentation processes require robust monitoring for optimal yield and quality.
  • Traditional methods may not capture dynamic changes in real-time.
  • Near-infrared (NIR) spectroscopy offers non-invasive, on-line data acquisition.

Purpose of the Study:

  • To investigate the utility of sample-sample two-dimensional correlation spectroscopy (SS-2DCoS) for monitoring fermentation state transitions.
  • To analyze on-line collected NIR spectra for process insights.
  • To correlate spectral changes with microbial metabolic and morphological shifts.

Main Methods:

  • Utilized on-line near-infrared (NIR) spectral data collected during fermentation.
  • Applied sample-sample two-dimensional correlation spectroscopy (SS-2DCoS) to analyze spectral variations.

Related Experiment Videos

  • Interpreted spectral patterns to identify changes in the fermentation system.

    • Main Results:

    • SS-2DCoS proved effective in extracting process information directly from on-line NIR spectra.
    • The complexity of the fermentation system precluded direct concentration profiling.
    • Fermentation state transitions, driven by microbial changes, were successfully identified.

    Conclusions:

    • SS-2DCoS is a valuable tool for real-time monitoring of fermentation processes.
    • The technique can detect critical state transitions linked to microbial dynamics.
    • This approach enhances understanding and control of complex biological systems.