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Related Experiment Videos

Modelling Bacillus cereus growth

E Chorin1, D Thuault, J J Cléret

  • 1A.D.R.I.A, 6 rue de l'université, Quimper, France.

International Journal of Food Microbiology
|March 20, 1998
PubMed
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This study models Bacillus cereus growth, finding temperature, pH, and water activity significantly impact its lag time and growth rate. These factors are crucial for predicting bacterial proliferation in food systems.

Area of Science:

  • Food microbiology
  • Bacterial growth modeling
  • Predictive modeling

Background:

  • Bacillus cereus is a toxigenic bacterium relevant to food safety.
  • Understanding its growth dynamics is essential for controlling food spoilage and preventing foodborne illnesses.
  • Environmental factors like temperature, pH, and water activity play critical roles in bacterial proliferation.

Purpose of the Study:

  • To develop a model predicting the growth of toxigenic Bacillus cereus strains.
  • To quantify the influence of temperature, pH, and water activity on bacterial growth parameters.
  • To establish a predictive framework for Bacillus cereus growth under various conditions.

Main Methods:

  • Bacterial growth was monitored in a model system.

Related Experiment Videos

  • Optical density (OD) measurements were converted to colony-forming units (CFU) using a calibration.
  • Growth curves were analyzed using the Gompertz function to determine lag time (λ) and growth rate (μ).

    • Main Results:

    • The Gompertz model successfully described Bacillus cereus growth curves.
    • Lag time (λ) and growth rate (μ) were significantly affected by temperature, pH, and water activity.
    • Specific relationships were established between these environmental factors and the estimated growth parameters at 20°C and 30°C.

    Conclusions:

    • Temperature, pH, and water activity are key predictors of Bacillus cereus growth dynamics.
    • The developed model provides a quantitative understanding of how these factors influence bacterial lag time and growth rate.
    • This research contributes to predictive microbiology for improved food safety management.