Researchers have developed a novel approach to automated detection and classification of harmful bacteria in food – allowing computers to learn what a pathogen might look like.

By equipping computers with sophisticated learning software and a laser scanner, colonies of pathogens such as listeria, staphylococcus, salmonella, vibrio and E. coli can be identified based on the optical properties of their colonies, and new unknown pathogens can be discovered.

“The sheer number of existing bacterial pathogens and their high mutation rate makes it extremely difficult to automate their detection,” said Dr M. Murat Dundar, assistant professor of computer science in the School of Science at Indiana University-Purdue University Indiana, and a principal investigator on the study.

“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.”

To detect and identify colonies of pathogens, the researchers used a prototype laser scanner, developed by Purdue University researchers. Without the new enhanced machine-learning approach, the light-scattering sensor used for classification of bacteria is unable to detect classes of pathogens not explicitly programmed into the system’s identification procedure.

“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,” said Dundar.

The study appears in the October issue of the journal Statistical Analysis and Data Mining.

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