用最少替换生成更健康、更便宜的日常餐食
Translating Dietary Standards into Healthy Meals with Minimal Substitutions
- 基于真实饮食数据构建34种餐食模板，生成符合营养目标的餐食
- 仅替换1-3种食材，营养提升10%，成本降低19%-32%
- 适合临床支持、公共健康和消费类应用落地
个性化饮食系统的重要目标是在不牺牲便利性和可负担性的前提下提升营养质量。本文提出一个端到端框架，将膳食标准转化为只需少量调整的完整餐食。基于135,491份真实餐食的美国国家膳食调查（WWEIA）数据，我们识别出34个可解释的餐食原型，并以此为条件，训练生成模型与分量预测器，以满足美国农业部（USDA）的营养目标。在原型内部对比中，生成餐食对推荐每日摄入量（RDI）的符合度提升47.0%，且与真实餐食结构高度相似。通过允许每餐1至3次食物替换，所生成餐食平均营养水平提高10%，成本降低19%-32%。该框架将膳食指南转化为现实、预算友好的餐食及简单替换方案，可支撑临床决策支持、公共卫生项目和消费者应用，实现可扩展、公平的日常营养改善。
原文摘要 · Abstract (English)
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.
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