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Related Experiment Videos

Shelf life prediction: status and future possibilities

T A McMeekin1, T Ross

  • 1Department of Agricultural Science, University of Tasmania, Australia. Tom.McMeekin@agsci.utas.edu.au

International Journal of Food Microbiology
|November 1, 1996
PubMed
Summary

Predictive microbiology models food spoilage, offering a scientific basis for shelf-life estimation devices. Developing these tools requires understanding microbial ecology for accurate food safety monitoring.

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Area of Science:

  • Food Science
  • Microbiology
  • Predictive Modeling

Background:

  • Current chemical detection technologies have limited application in food spoilage estimation and shelf-life determination.
  • Predictive microbiology offers a framework to forecast the behavior of spoilage organisms and spoilage progression in foods.
  • Accurate shelf-life prediction necessitates a comprehensive understanding of the food system's microbial ecology and physiology.

Purpose of the Study:

  • To explore the application of predictive microbiology in developing devices for monitoring food shelf life.
  • To review microbial ecology and physiology relevant to food spoilage.
  • To describe the potential benefits of predictive microbiology for shelf-life estimation.

Main Methods:

  • Development and validation of a modeling program to assess the impact of temperature, water activity, and pH on psychrotrophic pseudomonads growth rates.

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  • Review of microbial ecology and physiology relevant to food spoilage.
  • Discussion of necessary properties for shelf-life monitoring devices.

    • Main Results:

    • A modeling program was developed to predict the growth of spoilage pseudomonads under various conditions.
    • 'Bioindicators' were identified as potential spoilage monitors.
    • Preliminary evidence supports the concept of 'upper limiting bacterial growth' rates for bioindicator development.

    Conclusions:

    • Predictive microbiology provides a robust foundation for creating effective food shelf-life monitoring devices.
    • Understanding microbial ecology is crucial for accurate shelf-life predictions.
    • Bioindicators based on bacterial growth limits show promise for real-time spoilage detection.