Sep. 1, 2008 - Aug. 31, 2012Award Number
USDA - CSREESAmount Awarded
Lawrence D. Goodridge, Ph.D.
Colorado State University
Protection of the US food system during the 21st century is becoming an increasing challenge. The CDC estimates that 76 million illnesses, 325,000 hospitalizations, and 5,000 deaths occur annually due to foodborne pathogens (Mead et al. 1999). The USDA projects the resulting economic burden at more than $7 billion/yr (USDA 2008). Fresh fruits and vegetables have increasingly become responsible for many cases of foodborne illness. For example, between 1990 and 2003, there were at least 554 foodborne outbreaks associated with vegetables, and these outbreaks resulted in approximately 28,000 illnesses and several deaths (CSPI 2006). Clearly, there is an acute need to develop effective solutions to reduce the burden of foodborne disease related to the production of fresh produce. Currently, sampling methodologies and diagnostic testing of fresh produce to determine the presence of foodborne bacterial pathogens is accomplished using a "haphazard" approach in which a multitude of environmental samples is tested using a variety of methods, to ascertain the presence of the target organisms. In some cases (i.e. leafy greens) the product itself is tested, but standardized parameters such as what determines a lot of product, sampling size (especially from fields that contain hundreds of acres of product), and sampling methods (when, where, and how to sample), are nonexistent. Transformative technologies are needed to address the foodborne outbreaks associated with produce. These technologies should be designed such that they will be useful to prevent, detect, monitor and control potential food safety hazards during the growth, harvest and processing of fresh produce. During this proposal, methodologies that allow for rapid, sensitive, and reliable detection of produce-borne contamination will be developed. The objective of this project is to develop and validate a suite of sample preparation and diagnostics that will be capable of detecting foodborne pathogens and indicators (biological and chemical) of fecal contamination. A risk assessment model will be developed that will determine the types of agricultural water samples that are most likely to contain the pathogens (E. coli O157 and Salmonella spp.), and/or indicator organisms (fecal coliforms, E. coli, FRNA phages) of interest. As part of the sample preparation aspect of the proposal, we will develop methodology to allow for large volumes of sample (i.e. 10 to 50 L of water) to be tested (i.e. we will develop methods to concentrate microorganisms from large volumes of water, in a manner that allows for subsequent detection of the microorganisms), followed by methodology to effect rapid testing of the indicators and pathogens, and real time detection of chemical indicators (total organic carbon) of water quality. Finally, we will install the most robust large volume sampling and testing methodologies at selected producers to demonstrate the ability of the tests to effectively monitor the microbial safety of agricultural water. Communication regarding the results of this project will be accomplished through symposia, digital bulletins, peer review publications, and targeted farm visits.