ArticleCurrent research in food science2023
Using machine learning models to predict the quality of plant-based foods.
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 7 papers.
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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
7 citing papers in PubMed.
- Mechanistic Insights Into Color and Texture Stability of Frozen Tropical Vegetables: Processing, Modeling, and Preservation Strategies.Food science & nutrition · 2026Review
- Artificial Intelligence-Enabled Ingredient Substitution in Food Systems: A Review and Conceptual Framework for Sensory, Functional, Nutritional, and Cultural Optimization.Foods (Basel, Switzerland) · 2025Review
- A Review of Processing, Marketing, and Demographic Influence on Consumer Perception of Pulse Milk- Insights From Traditional and AI-Facilitated Research.Journal of food science · 2025Review
- The Healthy Fatty Index Allows for Deeper Insights into the Lipid Composition of Foods of Animal Origin When Compared with the Atherogenic and Thrombogenicity Indexes.Foods (Basel, Switzerland) · 2024Article
- Unlocking the nutritional potential of chickpea: strategies for biofortification and enhanced multinutrient quality.Frontiers in plant science · 2024Review
- Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market.Foods (Basel, Switzerland) · 2023Article
- Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data.Nutrients · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant-based foods (PBFs) are considered healthy, especially, minimally processed whole foods, fruits, whole grains, and legumes while highly processed PBFs maybe less nutritious. Educating consumers on nurient quality will help to guide their choices. This study was aimed at estimating and predicting the nutrient quality of PBFs using their Nutri-Score and micronutrient content. The NHANES (2017-2020) data shows the output for foods consumed in the US and their nutrient composition based on a 24-h recall. Though the Nutri-Score label has been used to discriminate food quality, it still needs to be implemented in most countries. It computes mostly macronutrients with less consideration for micronutrients which also contributes to product quality. ML methods used in this study combine the Nutri-Score grade and micronutrient content in predicting food quality. The FNDDS data of PBFs for 2017-2020 were split into training (n = 300) and testing (n = 74) datasets. Eight ML models were used to predict the Nutri-Score and the Nutri-Score grade of PBFs. Random forest (RF) and light gradient boost model (LightGBM) performed best with accuracy and coefficient of determination (R
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Registered trials
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