文章基本信息

标题：An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods
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作者：Tazman Davies ; Jimmy Chun Yu Louie ; Tailane Scapin 等
期刊名称：Nutrients
电子版ISSN：2072-6643
出版年度：2021
卷号：13
期号：9
DOI：10.3390/nu13093195
语种：English
出版社：MDPI Publishing
摘要：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 ( n = 8986) and test datasets ( n = 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 ( R 2 = 0.84 vs. R 2 = 0.68). Our findings highlight the opportunity to use machine learning to efficiently predict the fiber content of packaged products on a large scale.
关键词：endietary fiber;machine learning;computer science;public health