Rapid Detection Technologies for Foodborne Pathogens On-Demand Webcast

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Foodborne illness in the United States is an important health concern. Outbreaks highlight key deficiencies in the food distribution chain, and one of them is the lack of traceability methodology to track contaminated food back to the source in a time effective manner. One of the four critical areas under the FDA Food Modernization Safety Act is the need for better detection of and response to food safety problems. Common food testing strategies include traditional bacterial culture/plating of food samples to test for bacterial pathogen contamination, as well as advanced testing options including DNA and immunology-based assays. While these assays are sensitive and reliable, they suffer from key limitations including: the inability to to reliably distinguish viable bacteria in food samples, a requirement for comprehensive samle and assay preparation which in some cases requires 3-7 days to obtain a result, etc.

University and USDA scientists have been working to develop technologies that will enhance the ability to sample and test increasing amounts of foods as well as improve the detection and analysis of of foodborne pathogens. This webcast highlights several technologies including a light scanner imaging technology called BARDOT, for detection and analysis of foodborne pathogens, immobilized bacteriophage, FT-IR methods, and high-throughput biosensors for multiplexed foodborne pathogen detection.

Learning Objectives:

  • Participants will examine technologies developed for the rapdi detection and analysis of foodborne pathogens
  • Participants will learn about USDA and university applications of the rapid detection technologies for analysis of pathogens from foods

Intended Audience:

  • Product Developers
  • Company Management
  • Sales & Marketing Personnel
  • Plant Production Personnel
  • Quality Assurance Managers and Supervisors
  • Food Safety Specialists
  • Product manufacturers
  • Research Staff
  • Regulators
  • Academics
  • Students

Learning Level: Beginner, Intermediate, and Advanced


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