ArticleNutrients2021
An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.
Article in Nutrients, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Estimating Nutrient Composition of Packaged Foods Using Natural Language Processing and Optimization Modeling.Current developments in nutrition · 2026Article
- Correction: Davies et al. An Innovative Machine Learning Approach to Predict the Dietary Fiber Content of Packaged Foods.Nutrients · 2026Article
- Food Compass Score-10: validation of a method for evaluating the healthfulness of foods and beverages using ingredient list information.The American journal of clinical nutrition · 2025Article
- Artificial Intelligence Applications to Public Health Nutrition.Nutrients · 2023Article
- Predicting Unreported Micronutrients From Food Labels: Machine Learning Approach.Journal of medical Internet research · 2023Article
- Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis.Foods (Basel, Switzerland) · 2023Review
- Machine learning accurately predicts food exchange list and the exchangeable portion.Frontiers in nutrition · 2023Article
- Using machine learning models to predict the quality of plant-based foods.Current research in food science · 2023Article
- Machine learning models to predict micronutrient profile in food after processing.Current research in food science · 2023Article
- Socio-economic difference in purchases of ultra-processed foods in Australia: an analysis of a nationally representative household grocery purchasing panel.The international journal of behavioral nutrition and physical activity · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.