This work presents FoodScribe, which translates free-text meal descriptions into quantitative nutrient profiles by combining ingredient parsing with nutrient retrieval by querying the USDA FoodData Central (FDC) database.
Abstract
Efficiently summarizing dietary records at scale remains a persistent bottleneck in nutritional epidemiology. We present FoodScribe, which translates free-text meal descriptions into quantitative nutrient profiles by combining ingredient parsing with nutrient retrieval by querying the USDA FoodData Central (FDC) database. Benchmarked using three LLM providers using Nutribench dataset, FoodScribe completed annotation of 3,807 meal descriptions in 2.5 hours, a task otherwise requiring substantial manual effort from trained nutritionists. FoodScribe achieved accuracy across macronutrient estimation (F1=0.79-0.89), with models performing better for protein than fat estimation. Application to a Mediterranean diet intervention cohort indicated dietary shifts consistent with the intervention pattern based on model-derived estimates. Integration with metabolomics data suggested that fiber and vegetable intake were positively associated with a fecal metabolite cluster.
Background Dietary labels, calorie targets and broad food-group advice do not by themselves show whether nutrient functions are preserved when foods are excluded or substituted. This study developed a proposed nine-domain nutrient-function architecture to translate adult nutrient requirements into auditable food-source...
The increasing complexity of modern food products and dietary supplements has made it challenging for both consumers and healthcare professionals to interpret nutritional information and assess the potential health risks associated with these products. Modern food labeling schemes provide static and fragmented informat...
Dakshina P. D. S. D., Rupasighe W. A. R. K., Waduge N. P. et al.· 2026 6th International Confe...· 0 citations
Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain largely free text; readable by people but not directly computable. Existing recipe collections capture fragments of this information, but no shared representation links a re...
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Background/Objectives: In food-image nutrient estimation with vision-language models (VLMs), portion size is a dominant source of error, yet how a prompt shifts the estimated amount—and in which direction—remains uncharacterized. We tested whether a Japanese-dietitian prompt acts as a systematic, directional influence...
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BACKGROUND
In 2022, the High-Level Panel of Experts on Food Security and Nutrition highlighted persistent gaps in food consumption data and noted that policymakers are often unaware of existing but dispersed data sources. As part of its mandate, the Food and Agriculture Organization of the United Nations (FAO) collects...
A. Moltedo, Aydan Selek, Pauline Allemand et al.· Journal of NutriLife· 0 citations
Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually reported dietary intake, limiting their scalability in free-living settings. We propose a multimodal framework that replaces manual dietary logging...
V. Kondratyeva, Kamilia Zaripova, N. Navab et al.· 0 citations
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