Want to know how processed your food is? There’s an algorithm for that.
Northeastern researchers have been busy trying to better understand the links between “ultra-processed foods” and human health through the university-sponsored Foodome project.
As part of that Herculean effort, researchers with the the Center for Complex Network Research have now developed a machine learning algorithm they say accurately predicts the degree of processing in food products that make up the U.S. food supply. Their findings were published in Nature Communications in April.
The machine learning classifier, called FoodProX, uses nutritional labeling information provided by the U.S. Department of Agriculture’s Food and Nutrient Database for Dietary Studies as inputs to score the level of processing in a given food product.
The algorithm works by producing an output that represents the likelihood that a respective food falls into one of the four categories that are part of the NOVA food classification system—a system developed by researchers at the University of São Paulo, Brazil, that the researchers say is “widely used in epidemiological studies.”
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Photo by Matthew Modoono/Northeastern University