The reports for this project are available upon request.

In this project, we aim to expand the state-of-the-art of inspection strategy research to accommodate the unique characteristics of a particular sensitive type of imports: fresh produce. Our vision of this research is to have science-based intelligent inspection systems that can be used in a variety of fields, but not only the detection of “tainted” foodstuff problem. This research will develop highly adaptive inspection methodologies that over time can learn to detect new threats and will also easily incorporate new sensors and sources of information.

Problem description

Although the United States has initiated several private and public efforts to mitigate the risk of an agro-terrorism attack on the U.S. food supply, imported foods are increasingly becoming the weakest link. In recent years there have been several food recall incidences in the U.S. that originated from non-domestic sources. Some examples include: 1) a cyanide poisoning of Chilean grapes in 1987 that resulted in a major food recall from grocery stores in the U.S. with economic losses in the hundreds of millions of dollars, 2) the hepatitis A outbreak in green onions that occurred in November 2003. Shortly after the green onion produce was contaminated in Mexico, approximately 555 people with hepatitis A were identified and 13 specific restaurants were found to contain this pathogen in the U.S. Three people died as a result of this outbreak, and 3) the recent pet food contamination that was first reported in March 2007 caused sickness and death to many cats and dogs. The source of this contamination is now traced back to China. These incidences illustrate the potential vulnerability in the security matrix due to imported food products. Intelligent food defense systems provide a potential strategy with real time controls to mitigate food defense risk for imported products.

However, the adoption of intelligent technologies by private firms on a voluntary basis will only be economically viable if the expected economic returns are greater than the costs incurred. In this study, we propose to 1) plan an information environment that will be the backbone of a smart inspection framework and, 2) conduct a detailed cost-effective evaluation of a number of alternative intelligent system technologies using dynamic “real option” economic methods and to assess the feasibility of intelligent systems for the U.S.- Mexico fresh produce supply chain. Models developed can be extended to other industries and sectors. Results are anticipated to enhance inspection at the port-of-entry into the U.S. and to provide a joint disaster recovery and contingency plan to mitigate economic loss from international catastrophic food events.

We are working together with our partners to get data for some pilot experiments. At the mean time, we are developing dynamic inspection effort allocation model.

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