An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.

Journal: Nutrients
PMID: 34579072

Abstract

Underconsumption of dietary fiber is prevalent worldwide and is associated with multiple adverse health conditions. Despite the importance of fiber, the labeling of fiber content on packaged foods and beverages is voluntary in most countries, making it challenging for consumers and policy makers to monitor fiber consumption. Here, we developed a machine learning approach for automated and systematic prediction of fiber content using nutrient information commonly available on packaged products. An Australian packaged food dataset with known fiber content information was divided into training ( = 8986) and test datasets ( = 2455). Utilization of a k-nearest neighbors machine learning algorithm explained a greater proportion of variance in fiber content than an existing manual fiber prediction approach ( = 0.84 vs. = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.

Authors

  • Tazman Davies
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
  • Jimmy Chun Yu Louie
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
  • Tailane Scapin
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
  • Simone Pettigrew
    School of Psychology and Speech Pathology Curtin University, Western Australia.
  • Jason Hy Wu
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
  • Matti Marklund
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.
  • Daisy H Coyle
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW 2042, Australia.

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