PLANNING AND LEADERSHIP FOR A BETTER WORLD
PLANNING AND LEADERSHIP FOR A BETTER WORLD | Table of Contents: |
Current approaches to food testing lack the technology and the efficiency to screen large samples for bacteria. Even with the FDA's many regulations, food-borne pathogens kill thousands of people in the United States each year—hundreds of thousands more are sickened.
Salmonella bacteria are one of the more common causes of food-borne illness.
Deadly bacteria like E. coli lurk unseen in everything from eggs to spinach to peanut butter. These bacteria can cause violent illnesses and even death. A new bacteria detection method from scientists at the School of Science at Indiana University-Purdue University Indianapolis (IUPUI) and the Bindley Bioscience Center at Purdue University could improve the rapid detection of pathogens—thereby saving lives and improving our food supply.
The scientists have developed an innovative, automated way to detect and classify the dangerous bacteria found in food. Their sophisticated statistical approach enables computers to improve their abilities to test samples. With complex new formulas, technological progress called "machine-learning" occurs: Computers can identify even unknown classes of food pathogens. The computers are also highly capable of detecting known pathogens, such as listeria, staphylococcus, salmonella, vibrio and E. coli.
"The sheer number of existing bacterial pathogens and their high mutation rate make it extremely difficult to automate their detection," says M. Murat Dundar, Ph.D., assistant professor of computer science in the School of Science at IUPUI, and the leader of the research team. "There are thousands of different bacteria subtypes, and you can't collect enough subsets to add to a computer's memory so it can identify them when it sees them in the future. Unless we enable our equipment to modify detection and identification based on what it has already seen, we may miss discovering isolated or even major outbreaks."
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