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Predictive microbiology: providing a knowledge-based framework for change management

Citation

McMeekin, TA and Ross, T, Predictive microbiology: providing a knowledge-based framework for change management, International Journal of Food Microbiology, 78, (1) pp. 133-153. ISSN 0168-1605 (2002) [Refereed Article]

DOI: doi:10.1016/S0168-1605(02)00231-3

Abstract

This contribution considers predictive microbiology in the context of the Food Micro 2002 theme, "Microbial adaptation to changing environments". To provide a reference point, the state of food microbiology knowledge in the mid-1970s is selected and from that time, the impact of social and demographic changes on microbial food safety is traced. A short chronology of the history of predictive microbiology provides context to discuss its relation to and interactions with hazard analysis critical control point (HACCP) and risk assessment. The need to take account of the implications of microbial adaptability and variable population responses is couched in terms of the dichotomy between classical versus quantal microbiology introduced by Bridson and Gould [Lett. Appl. Microbiol. 30 (2000) 95]. The role of population response patterns and models as guides to underlying physiological processes draws attention to the value of predictive models in development of novel methods of food preservation. It also draws attention to the paradox facing today's food industry that is required to balance the "clean, green" aspirations of consumers with the risk, to safety or shelf life, of removing traditional barriers to microbial development. This part of the discussion is dominated by consideration of models and responses that lead to stasis and inactivation of microbial populations. This highlights the consequence of change on predictive modelling where the need is now to develop interface and non-thermal death models to deal with pathogens that have low infective doses for general and/or susceptible populations in the context of minimal preservation treatments. The challenge is to demonstrate the validity of such models and to develop applications of benefit to the food industry and consumers as was achieved with growth models to predict shelf life and the hygienic equivalence of food processing operations. © 2002 Elsevier Science B.V. All rights reserved.

Item Details

Item Type:Refereed Article
Research Division:Biological Sciences
Research Group:Microbiology
Research Field:Microbiology not elsewhere classified
Objective Division:Health
Objective Group:Public Health (excl. Specific Population Health)
Objective Field:Food Safety
Author:McMeekin, TA (Professor Thomas McMeekin)
Author:Ross, T (Associate Professor Tom Ross)
ID Code:24101
Year Published:2002
Web of Science® Times Cited:77
Deposited By:Agricultural Science
Deposited On:2002-08-01
Last Modified:2003-05-14
Downloads:0

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