ArticleCurrent research in food science2023
Machine learning models to predict micronutrient profile in food after processing.
Article in Current research in food science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- A Methodological Framework for Aggregating Branded Food Composition Data in mHealth Nutrition Databases: A Case Presentation.Nutrients · 2026Article
- A Multinutrient Clustering Framework for Personalized Food Recommendations.International journal of food science · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Single nucleotide polymorphism information estimates breed and variety composition ratio in food.Current research in food science · 2026Article
- Integration of Colorimetric Sensing Dyes and Multi-Substrate Arrays for Food Quality and Safety Monitoring: Design, Optimization, and Analysis.Comprehensive reviews in food science and food safety · 2026Review
- Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention.Nutrients · 2025Review
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine.Journal of agricultural and food chemistry · 2024Article
- Ontological how and why: action and objective of planned processes in the food domain.Frontiers in artificial intelligence · 2023Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The information on nutritional profile of cooked foods is important to both food manufacturers and consumers, and a major challenge to obtaining precise information is the inherent variation in composition across biological samples of any given raw ingredient. The ideal solution would address precision and generability, but the current solutions are limited in their capabilities; analytical methods are too costly to scale, retention-factor based methods are scalable but approximate, and kinetic models are bespoke to a food and nutrient. We provide an alternate solution that predicts the micronutrient profile in cooked food from the raw food composition, and for multiple foods. The prediction model is trained on an existing food composition dataset and has a 31% lower error on average (across all foods, processes and nutrients) than predictions obtained using the baseline method of retention-factors. Our results argue that data scaling and transformation prior to training the models is important to mitigate any yield bias. This study demonstrates the potential of machine learning methods over current solutions, and additionally provides guidance for the future generation of food composition data, specifically for sampling approach, data quality checks, and data representation standards.
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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.