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United States Department of Agriculture

Agricultural Research Service

Title: Approaches for Modeling Thermal Inactivation of Foodborne Pathogens

Author
item Juneja, Vijay

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: June 17, 2004
Publication Date: August 24, 2004
Citation: Juneja, V.K. 2004. Approaches for modeling thermal inactivation of foodborne pathogens. Meeting Abstract.

Technical Abstract: A variety of theories and data analysis approaches are available for constructing inactivation models for foodborne pathogens. For over a century, it has been recognized that, within a fixed environment, microbial cells become inactivated at a nearly exponential rate. This observation suggests that the logarithm of the percentage of surviving cells versus time (the survival curve) would be linear. However, the linearity often does not extend throughout the entire range of experimental data observed in laboratories. Rather, many survival curves display certain general non-linear patterns, which need to be mathematically modeled to obtain reasonably accurate predictions of bactarial inactivation over time. Consequently, many different nonlinear functions have been developed and used for modeling survival curves. In general, modeling implies not only accurately predicting the survival rates, but also estimating the uncertainty of inactivation or range of possible survival rates under certain environmental conditions. Deriving such models entails estimating the variation of survival rates caused by between-strain effects or small inherent variations from specified conditions. In addition, the data are often correlated because of common experimental treatments or effects that impact results on subsets of the data. Thus,to capture the full extent of the variability and the impact of the design-induced correlations on the uncertainty of the estimated parameters, mixed-effect modeling (which accounts for random variation) is needed. This presentation will provide a discussion of the microbiological and statistical assumptions used for modeling lethality of foodborne pathogens.

Last Modified: 8/1/2014
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