Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: June 16, 2007
Publication Date: June 17, 2007
Citation: Juneja, V.K. 2007. Use of U.S. Department of Agriculture - Pathogen Modeling Program and the Predictive Microbiology Information Portal . Meeting Abstract. Technical Abstract: The science of Predictive Microbiology is based on the assumption that bacterial behavior is reproducible, and that it can be quantified by characterizing the environmental factors that affect growth, survival, and inactivation. The U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) Pathogen Modeling Program (PMP; http://www.ars.usda.gov/naa/errc/mfsru/pmp) is a software package of microbial models and a research product of the Microbial Food Safety Research Unit (MFS) that is meeting the needs of ARS customers in government, industry and academia. The PMP 7.0 currently contains 65% food and 35% broth models and includes both static and dynamic temperature models. These models allow users to predict food formulation, processing and handling conditions that control the growth, survival and death of various bacterial foodborne pathogens. The PMP has become a premier international modeling tool that is also used by several US food processing companies in the management of food safety systems and is downloaded more than 8,000 times each year in over 35 countries. Once downloaded, user-friendly features allow the client to easily input food-relevant criteria and then to receive predictions about how pathogenic bacteria react to specific food environments. To further assist food processors in meeting regulatory requirements, references are provided for each model via direct Internet access to PDF files. A Predictive Microbiology Information Portal (PMIP) has also been developed by MFS at the Eastern Regional Research Center to assist small and very small processing companies in the use and interpretation of PMP models. In addition, PMIP can assist in locating and retrieving regulatory information, predictive models, research data and numerous food safety related links associated with the models.