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[2602.13502] Translating Dietary Standards into Healthy Meals with Minimal Substitutions
Summary
This article presents a framework for creating nutritious meals that adhere to dietary standards with minimal substitutions, enhancing both health and affordability.
Why It Matters
As dietary-related health issues rise, this research provides a scalable solution to improve nutrition in everyday meals. By leveraging AI, it offers a practical approach to meet USDA nutritional targets while considering cost and convenience, making it relevant for public health initiatives and consumer applications.
Key Takeaways
- The framework generates meals that meet USDA nutritional targets with minimal substitutions.
- Generated meals are 10% more nutritious and reduce costs by 19-32% on average.
- The approach can support public health programs and consumer apps for better nutrition.
Computer Science > Artificial Intelligence arXiv:2602.13502 (cs) [Submitted on 13 Feb 2026] Title:Translating Dietary Standards into Healthy Meals with Minimal Substitutions Authors:Trevor Chan, Ilias Tagkopoulos View a PDF of the paper titled Translating Dietary Standards into Healthy Meals with Minimal Substitutions, by Trevor Chan and 1 other authors View PDF Abstract:An important goal for personalized diet systems is to improve nutritional quality without compromising convenience or affordability. We present an end-to-end framework that converts dietary standards into complete meals with minimal change. Using the What We Eat in America (WWEIA) intake data for 135,491 meals, we identify 34 interpretable meal archetypes that we then use to condition a generative model and a portion predictor to meet USDA nutritional targets. In comparisons within archetypes, generated meals are better at following recommended daily intake (RDI) targets by 47.0%, while remaining compositionally close to real meals. Our results show that by allowing one to three food substitutions, we were able to create meals that were 10% more nutritious, while reducing costs 19-32%, on average. By turning dietary guidelines into realistic, budget-aware meals and simple swaps, this framework can underpin clinical decision support, public-health programs, and consumer apps that deliver scalable, equitable improvements in everyday nutrition. Comments: Subjects: Artificial Intelligence (cs.AI); Other Quanti...
