New method to protect food from bioterror revealed

Researchers have recently designed and implemented a new statistical approach to improve their ability to detect bacterial contamination in the food supply the could come from bioterror attacks.

Scientists from the School of Science at Indiana - Purdue University Indianapolis and the Bindley Bioscience Center at Purdue University are using new formulas to propel machine-learning, enabling the identification of known and unknown kinds of food pathogens, according to Their study appears in the October issue of Statistical Analysis and Data Mining.

M. Murat Dundar, an assistant professor of computer science in the School of Science in IUPUI and the principal investigator of the study, explained the difficulty in the current approach to

“The sheer number of existing bacterial pathogens and their high mutation rate makes it extremely difficult to automate their detection,” Dundar said, reports. “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.”

The researchers have used a prototype laser scanner developed at Purdue to identify colonies of listeria, staphylococcus, salmonella, vibrio and E. Coli based solely on their optical qualities. This would be impossible without the new enhanced machine-learning approach. Without it, the scanner would have to have the identifications of pathogens programmed directly into it.

“We are very excited because this new machine-learning approach is a major step towards a fully automated identification of known and emerging pathogens in real time, hopefully circumventing full-blown, food-borne illness outbreaks in the near future. Ultimately we would like to see this deployed to tens of centers as part of a national bio-warning system,” Dundar said, according to