Integrating National Resource Information and Food System Signals to Identify Novel Methods for Control of Microbial Contamination in Spinach

NON-TECHNICAL SUMMARY: To reduce the incidence of produce related foodborne illnesses and protect the integrity of the nation's food supply, it is critical to identify new and improved ways to decrease pre-harvest contamination of produce by foodborne pathogens. While underutilized in food safety research and practice, it is well known that local factors, such as soil characteristics, weather and wildlife density, influence the probability of isolating microorganisms from produce and agricultural fields. Spatially explicit information on these and many other factors is readily available from the National Resources Information Databases. The current project will utilize these Databases and integrate them with the food system signals on the local production practices and surveillance through strategic pairing of the field detection of microbial contaminants in produce fields and the sophisticated spatial and statistical modeling approaches. This project will focus on major foodborne pathogens (Listeria monocytogenes, Salmonella, Escherichia coli O157:H7) and indicator microorganisms in spinach production systems in Colorado and Texas. The utilized approach will allow identification of novel strategies to control foodborne pathogens in spinach at the pre-harvest level based on appreciation of the local management practices and ecological conditions. The resulting identified innovative approaches to improve microbial safety of produce and the related anticipated reduction in the incidence of human foodborne illness will benefit the produce production industry and society as a whole.

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APPROACH: Spinach samples will be collected from produce fields in Texas and Colorado and tested for contamination with Listeria monocytogenes, Salmonella, Escherichia coli O157:H7, and indicator microorganisms (Listeria spp., generic E. coli, and coliforms). For each sampling location, we will also record geographic coordinates and the pre-harvest food system signals on the local production practices and surveillance occurring in the period before sample collection. Geospatial analysis will be performed through spatial modeling and rigorous statistical modeling to develop microorganism-specific statistical models. These models will thus predict the contamination status of spinach based on data extracted from the National Resource Information Databases and pre-harvest food system signals. The models will be validated through an assessment of their predictive ability, and used to identify new and improved control measures to reduce the probability of spinach contamination, closing the circle in this multidisciplinary study embracing field microbial detection and innovative use of geospatial technology and analysis.