"The most significant accomplishment ... was the design of an automated BARDOT system and related algorithm."

Improved Detection Techniques for Foodborne Pathogens: Bacterial Rapid Detection using Optical Scattering Technology (BARDOT)

Investigator: Arun K. Bhunia (Department of Food Science)

Project Report 2007 - 2008

» Download Project Report 2007 - 2008

Project Rationale

The Centers for Disease Control and Prevention (CDC) estimates that 76 million people get sick, more than 300,000 are hospitalized, and 5,000 Americans die each year from foodborne pathogen infections. Preventing foodborne illnesses remains a major public health challenge. Listeria monocytogenes, Escherichia coli, and Salmonella are three major foodborne pathogens of concern in the U.S. L. monocytogenes, along with Salmonella and Toxoplasma, are responsible for more than 75 percent of the foodborne diseases and 1,500 deaths every year compared to the other known pathogens. There has been an increase in foodborne illnesses, multiple outbreaks, product recalls, and loss of lives as a result of the association with pathogens in processed, ready-to-eat food. Bacterial contamination in food not only places the public at risk, it is costly to companies due to loss of production time, product recalls, and liability.

For detecting and evaluating foods contaminated with L. monocytogenes or E. coli, the USDA/FSIS recommends initial enrichment and subsequent plating on a selective agar medium, which is often followed by identification procedures. These procedures are time-consuming, lasting more than five to seven days. The present industrial demand is to increase the speed of detection, decrease economical losses, and minimize public health concerns. Our main objective was to develop a simple optical light scattering sensory method to reduce the time to identify pathogenic bacteria after plating.

Project Objectives

  • Design and develop a prototype of a fully automated BActeria Rapid Detection using Optical scattering Technology (BARDOT) system to locate, capture, and classify foodborne pathogenic bacteria.
  • Acquire scatter images of colonies of select foodborne bacterial, including pathogens.
  • Analyze bacterial colonies of different foodborne bacteria on non-selective and selective agar media.
  • Validate the technology by using both inherently contaminated food samples and samples that have been contaminated with selected pathogens.
  • Analyze cellular composition, cell arrangement, refractive index ,and colony contents using electron microscopy, FT-IR or GC-MS.
  • Analyze the scatter signal images using "standard feature extraction" and "moments of shape analysis" methods.

Project Highlights

The most significant accomplishment of fiscal year 2007-2008 was the design of an automated BARDOT system and related algorithm. This system, including hardware and software, was redesigned and redeveloped, and a colony counter and locator were constructed to provide colony counts for each plate. To achieve that, we added a pair of illumination lights and a CCD (cooled coupled device) camera so that automatic colony counts and the precise location of each colony are visible on a computer monitor. The automated BARDOT system prototype (which was manufactured by the local company En'Urga) incorporates the colony locator (which locates a colony via a line scanner), the forward-scatterometer, and an automated classification package into a stand-alone system.