The Centers for Disease Control and Prevention has recently reported that an estimated 48 million cases of foodborne illness, 128,000 hospitalizations, and 3,000 deaths occur each year from foodborne microorganisms. In addition to human suffering, foodborne illnesses also have a substantial economic impact in the United States. The annual cost of foodborne illness in the U.S. is estimated at $89 billion for loss of productivity, other economic losses and medical expenses. Predictive microbiology and quantitative microbial risk assessment (QMRA) are rapidly developing scientific disciplines that use mathematical equations, numerical data, and expert opinion to estimate the presence, survival, growth, and death of microbes in foods. These models allow for the prediction of the safety of a product, based on the entire sequence of events up to consumption. They provide a framework for identifying critical data gaps and evaluating the effectiveness of risk-reduction strategies. As part of this project, predictive models will be built and validated for appropriate commodity/pathogen pairings. These developed models will be validated using real-life scenarios, whenever possible. The models generated for one commodity can be used to guide a series of experiments to validate the model for different, closely related commodities. Following the development of models, expert opinion, industry, experimentally derived and literature data for processing and handling conditions to the point of consumption can be integrated into risk assessment models to estimate changes in microbial population dynamics. The studies proposed here will be the first comprehensive attempt to develop risk-based strategies leading to effective control of pathogens from the farm through to consumption across all food commodities in the US. Additional expected outcomes include the use of microbiological data to develop risk-based models that can be used to better predict microbial contamination and predict the reduction of pathogens in foods due to application of various control strategies. It is expected that the outcomes of this project will contribute to the long-term profitability and sustainability of the food industry as a whole by making accessible a suite of new tools with which the microbial safety of foods can be enhanced.
|Effective start/end date||9/3/13 → 9/30/17|
- National Institute of Food and Agriculture (National Institute of Food and Agriculture (NIFA))
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.