Literature DB >> 7873329

Predictive microbiology.

T Ross1, T A McMeekin.   

Abstract

Predictive microbiology is based upon the premise that the responses of populations of microorganisms to environmental factors are reproducible, and that by considering environments in terms of identifiable dominating constraints it is possible, from past observations, to predict the responses of those microorganisms. Proponents claim that predictive microbiology offers many benefits to the practice of food microbiology, and there is growing interest internationally. This review considers the origins, benefits and approaches to predictive microbiology and critically considers limitations and potential solutions. It is suggested that the traditional delineation between kinetic and probabilistic models is artificial, and that the two approaches represent the opposite ends of a spectrum of modelling needs. It is concluded: that despite the complexity of many food systems predictive modelling can be successfully applied; that strategies based on predictive models can simplify problems and allow useful predictions and analyses to be made; that the full potential of the technique has not yet been realised; and that "predictive microbiology" may be seen as providing a rational framework for understanding the microbial ecology of food.

Mesh:

Year:  1994        PMID: 7873329     DOI: 10.1016/0168-1605(94)90155-4

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  13 in total

1.  Analysis of the influence of environmental parameters on Clostridium botulinum time-to-toxicity by using three modeling approaches.

Authors:  D W Schaffner; W H Ross; T J Montville
Journal:  Appl Environ Microbiol       Date:  1998-11       Impact factor: 4.792

2.  Predictive modelling of Lactobacillus casei KN291 survival in fermented soy beverage.

Authors:  Dorota Zielińska; Zielińska Dorota; Danuta Kołożyn-Krajewska; Kołożyn-Krajewska Danuta; Antoni Goryl; Goryl Antoni; Ilona Motyl
Journal:  J Microbiol       Date:  2014-02-01       Impact factor: 3.422

3.  Modeling yeast spoilage in cold-filled ready-to-drink beverages with Saccharomyces cerevisiae, Zygosaccharomyces bailii, and Candida lipolytica.

Authors:  Alyce Stiles Battey; Siobain Duffy; Donald W Schaffner
Journal:  Appl Environ Microbiol       Date:  2002-04       Impact factor: 4.792

4.  Statistical Package for Growth Rates Made Easy.

Authors:  Portia Mira; Miriam Barlow; Juan C Meza; Barry G Hall
Journal:  Mol Biol Evol       Date:  2017-12-01       Impact factor: 16.240

5.  Growth limits of Listeria monocytogenes as a function of temperature, pH, NaCl, and lactic acid.

Authors:  S Tienungoon; D A Ratkowsky; T A McMeekin; T Ross
Journal:  Appl Environ Microbiol       Date:  2000-11       Impact factor: 4.792

6.  Influence of stress on individual lag time distributions of Listeria monocytogenes.

Authors:  L Guillier; P Pardon; J-C Augustin
Journal:  Appl Environ Microbiol       Date:  2005-06       Impact factor: 4.792

7.  Applicability of an Arrhenius model for the combined effect of temperature and CO(2) packaging on the spoilage microflora of fish.

Authors:  K P Koutsoumanis; P S Taoukis; E H Drosinos; G J Nychas
Journal:  Appl Environ Microbiol       Date:  2000-08       Impact factor: 4.792

8.  Modeling the growth of Staphylococcus aureus as affected by black zira (Bunium persicum) essential oil, temperature, pH and inoculum levels.

Authors:  Abdollah Jamshidi; Saeid Khanzadi; Majid Azizi; Mohammad Azizzadeh; Mohammad Hashemi
Journal:  Vet Res Forum       Date:  2014       Impact factor: 1.054

9.  Leuconostoc mesenteroides growth in food products: prediction and sensitivity analysis by adaptive-network-based fuzzy inference systems.

Authors:  Hue-Yu Wang; Ching-Feng Wen; Yu-Hsien Chiu; I-Nong Lee; Hao-Yun Kao; I-Chen Lee; Wen-Hsien Ho
Journal:  PLoS One       Date:  2013-05-21       Impact factor: 3.240

10.  A study on the physicochemical parameters for Penicillium expansum growth and patulin production: effect of temperature, pH, and water activity.

Authors:  Joanna Tannous; Ali Atoui; André El Khoury; Ziad Francis; Isabelle P Oswald; Olivier Puel; Roger Lteif
Journal:  Food Sci Nutr       Date:  2015-12-16       Impact factor: 2.863

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