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Microbial modeling in foods

R C Whiting1

  • 1Eastern Regional Research Center, U.S. Department of Agriculture, Philadelphia, PA 19118, USA.

Critical Reviews in Food Science and Nutrition
|November 1, 1995
PubMed
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Predictive food microbiology uses mathematical and statistical models to forecast microbial growth or decline in foods. These predictive microbiology models are essential for food safety and Hazard Analysis, Critical Control Point (HACCP) planning.

Area of Science:

  • Food microbiology
  • Mathematical modeling
  • Statistical analysis

Background:

  • Predictive food microbiology integrates microbiology, mathematics, and statistics.
  • Microbial models predict microbial growth or decline under specific environmental conditions.
  • Models are crucial for food safety and risk assessment.

Purpose of the Study:

  • To review current predictive models for food-borne microorganisms, especially pathogens.
  • To discuss the applications of these microbial models in food safety.
  • To highlight the importance of predictive microbiology in HACCP programs.

Main Methods:

  • Development of primary models (e.g., Gompertz function, exponential growth) describing microbial changes over time.
  • Utilization of secondary models (e.g., response surface, Arrhenius relationships) linking model parameters to environmental conditions.

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  • Integration of primary and secondary models into tertiary level applications like software or expert systems.

    • Main Results:

    • Primary models quantify microbial dynamics (growth, survival, inactivation).
    • Secondary models elucidate the influence of environmental factors (temperature, pH) on microbial kinetics.
    • Tertiary models provide user-friendly tools for predicting microbial behavior.

    Conclusions:

    • Predictive microbial models are vital for food safety management.
    • These models aid in Hazard Analysis and Critical Control Point (HACCP) program development.
    • Ongoing model development enhances our ability to manage food-borne pathogens.