INDIANA U. (US) — A new approach that uses machine learning to detect harmful bacteria in food will allow for better identification of known—and unknown—classes of food pathogens.
“The sheer number of existing bacterial pathogens and their high mutation rate makes it extremely difficult to automate their detection,” says M. Murat Dundar, assistant professor of computer science at Indiana University-Purdue University Indianapolis.
“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 study is published in the October issue of the journal Statistical Analysis and Data Mining.
To detect and identify colonies of pathogens such as listeria, staphylococcus, salmonella, vibrio, and E. coli based on the optical properties of their colonies, 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,” says Dundar.
“Ultimately we would like to see this deployed to tens of centers as part of a national bio-warning system.
“Our work is not based on any particular property of light scattering detection and therefore it can potentially be applied to other label-free techniques for classification of pathogenic bacteria, such as various forms of vibrational spectroscopy,” adds Bartek Rajwa, the Purdue principal investigator of the study.
Dundar and colleagues believe this methodology can be expanded to the analysis of blood and other biological samples as well.
This study was supported by a grant from the National Institute of Allergy and Infectious Diseases.
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