ArticleNutrients2025
Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients.
Article in Nutrients, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Digital and Technology-Enabled Approaches in Dietary Assessment: Addressing Bias, Error, and Feasibility in Population- and Community-Based Research.Advances in nutrition (Bethesda, Md.) · 2026Review
- AGREE-YOLO: A Framework for Seafood Recognition and Cross-Cultural Gastronomic Recommendation.Foods (Basel, Switzerland) · 2026Article
- Artificial intelligence-driven food and nutrition systems: from smart food production to personalized nutrition.Frontiers in nutrition · 2026Article
- A comparative study of vision-language models for food ingredient recognition and nutrient estimation.Current research in food science · 2026Article
- Machine Learning-Driven Precision Nutrition: A Paradigm Evolution in Dietary Assessment and Intervention.Nutrients · 2025Review
- Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight.Nutrients · 2025Article
- A Behavioral Science-Informed Agentic Workflow for Personalized Nutrition Coaching: Development and Validation Study.JMIR formative research · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
BACKGROUND/
objectivesIdentifying and decomposing compound ingredients within meal plans presents meal customization and nutritional analysis challenges. It is essential for accurately identifying and replacing problematic ingredients linked to allergies or intolerances and helping nutritional evaluation.
methodsThis study explored the effectiveness of three large language models (LLMs)-GPT-4o, Llama-3 (70B), and Mixtral (8x7B), in decomposing compound ingredients into basic ingredients within meal plans. GPT-4o was used to generate 15 structured meal plans, each containing compound ingredients. Each LLM then identified and decomposed these compound items into basic ingredients. The decomposed ingredients were matched to entries in a subset of the USDA FoodData Central repository using API-based search and mapping techniques. Nutritional values were retrieved and aggregated to evaluate accuracy of decomposition. Performance was assessed through manual review by nutritionists and quantified using accuracy and F1-score. Statistical significance was tested using paired
resultsResults showed that large models-both Llama-3 (70B) and GPT-4o-outperformed Mixtral (8x7B), achieving average F1-scores of 0.894 (95% CI: 0.84-0.95) and 0.842 (95% CI: 0.79-0.89), respectively, compared to an F1-score of 0.690 (95% CI: 0.62-0.76) from Mixtral (8x7B).
conclusionsThe open-source Llama-3 (70B) model achieved the best performance, outperforming the commercial GPT-4o model, showing its superior ability to consistently break down compound ingredients into precise quantities within meal plans and illustrating its potential to enhance meal customization and nutritional analysis. These findings underscore the potential role of advanced LLMs in precision nutrition and their application in promoting healthier dietary practices tailored to individual preferences and needs.
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