An Essay On The Unrealized Potential Of Predictive Microbiology

McMeekin, TA; McKellar, R.C.; Lu, X.

doi:10.1201/9780203503942.ch12

Cited by 3 publications

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“…Roberts 5 recounted their belief that "models relevant to broad categories of foods would greatly reduce the need for ad hoc microbiological examination and enable predictions and quality and safety to be made more speedily with considerable financial benefit". Roberts and Jarvis' vision may now be realised in ComBase, as highlighted by McMeekin 6 , who considered that "properly supported, the COMBASE initiative will be a watershed in the evolution of predictive modeling and its widespread application".…”

Section: The Future Of Combasementioning

confidence: 99%

The ComBase initiative

Baranyi¹,

Tamplin²,

Ross³

2004

Microbiol. Aust.

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Food safety professionals in industry, academia and regulatory agencies increasingly use computer modelling tools to improve food safety and shelf life (including the development of Hazard Analysis Critical Control Point (HACCP) plans) and to support microbial food safety risk assessments. Earlier this year, we conducted workshops in Melbourne and Sydney to introduce Australian scientists to one such tool, ComBase, and to provide training in its use

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Section: The Future Of Combasementioning

confidence: 99%

The ComBase initiative

Baranyi¹,

Tamplin²,

Ross³

2004

Microbiol. Aust.

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Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Guillier

Pardon

Augustin

2005

Appl Environ Microbiol

114

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The effects of nine common food industry stresses on the times to the turbidity (T d ) distribution of Listeria monocytogenes were determined. It was established that the main source of the variability of T d for stressed cells was the variability of individual lag times. The distributions of T d revealed that there was a noticeable difference in response to the stresses encountered by the L. monocytogenes cells. The applied stresses led to significant changes of the shape, the mean, and the variance of the distributions. The variance of T d of wells inoculated with single cells issued from a culture in the exponential growth phase was multiplied by at least 6 and up to 355 for wells inoculated with stressed cells. These results suggest stress-induced variability may be important in determining the reliability of predictive microbiological models.

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Application of models and other quantitative microbiology tools in predictive microbiology

Legan

2007

Modelling Microorganisms in Food

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An Essay On The Unrealized Potential Of Predictive Microbiology

Cited by 3 publications

References 0 publications

The ComBase initiative

The ComBase initiative

Influence of Stress on Individual Lag Time Distributions of Listeria monocytogenes

Application of models and other quantitative microbiology tools in predictive microbiology

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