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Using AI To Study Food Contamination
June 3, 2020

According to statistics from the U.S. Food & Drug Administration (FDA), insect pests can contaminate as much as ten percent of the total food produced in the United States. Currently, the identification process of the specific species involved usually requires a food inspection analyst with years of training to determine the microscopic differences.

Now, according to information provided by the University of Michigan-Flint (UM-Flint), research is underway to study the use of artificial intelligence (AI) to identify food contaminating beetles.

Halil Bisgin, an assistant professor of computer science in the College of Arts & Sciences at UM-Flint with expertise in data mining and machine learning, is working with the FDA to create an AI system that automatically detects insect contaminants from images. The research project focused on 15 of the most common beetle species detected in food inspections.

“We cropped the images into smaller pieces because, with processed food, the whole beetle will probably not remain intact,” Prof. Bisgin explained. “I also used the Great Lakes supercomputer at U-M Ann Arbor to train my model because this is a very computationally demanding process.”

With an overall accuracy rating of 80 percent, Bisgin’s program can quickly identify the species contaminating stored foods, which in turn informs possible causes and solutions to the infestation.

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