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Control of Food-Borne Pathogens in Pre- and Post-Harvest Environments


<ul> <LI> Develop or improve methods for control or elimination of pathogens in pre-and post harvest environments including meat, poultry, seafood, fruits and vegetables and nutmeats.<LI> Develop and validate mathematical modeling to gain understanding of pathogen behavior in macro and micro-environments. <LI> Investigate factors leading to the emergence, persistence and elimination of antimicrobial resistance in food processing and animal production environments.

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Non-Technical Summary: <BR>Use of predictive modeling and quantitative microbial risk assessment tools are gaining increased acceptance both by the food industry and by regulatory agencies. Despite this increased acceptance, the number of academic researchers actively involved in pioneering the use of these tools is very limited. Examples of the sort of problems currently under investigation in Dr Schaffner's lab include: modeling and assessing the risk posed by the growth of Salmonella in cut tomatoes; simulating the transmission and risk posed by norovirus in foodservice settings; assessing the risk of low levels of Salmonella in peanut butter; and modeling and assessing the risk E. coli O157:H7 in leafy greens from field to fork. <P> Approach: <BR> Our standard procedure in developing predictive models and quantitative risk assessments is to start by surveying the literature, including models available in the USDA ARS Pathogen Modeling Program, and models and data available in ComBase. Available data are then converted into comparable units and combined into statistical distributions. Briefly, the first step is to conduct a literature search and identify studies as sources for growth rates for the relevant commodity-pathogen combinations at different temperatures. Studies presenting growth data on the commodity of interest, as well studies presenting growth data for commodity-associated pathogen strains on laboratory media are selected for further analysis. Growth parameters are extracted directly from tables or growth curves (by superimposing a regression line over the exponential phase of growth). Data are typically modeled using a square-root or Ratkowsky equation relating the square-root of the bacterial growth rate to storage temperature (T). Our quantitative microbial risk assessment technique consists of a literature search where data are collected by searching medical and biological databases for documents related to the topic under study. Software is used to convert graphical data to numerical form. Numerical data are combined wherever appropriate (i.e. where data had approximately the same range and peak). Data are translated into appropriate discrete or probability distribution functions. Numerical data are log transformed and histograms are generated for both literature and experimental data. Quantitative risk assessment models are created and results for simulated input distributions as well as final results are obtained by running iterations of the simulations.

Schaffner, Donald
Rutgers University
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