A framework for recipe data structure with applications for culinary and nutritional insights

Researchers at IIIT-Delhi's Complex Systems Laboratory released RecipeDB2, a structured compilation of 128,942 recipes and 35,474 ingredients spanning 32 regions and 99 countries, according to a new arXiv paper (2609.22099v1). The framework parses ingredient phrases into seven culinary attributes with a transformer-based named-entity model, links ingredients to USDA reference tables via a BERT embedding strategy that scored F1 = 87.90 on a manually adjudicated set of the 200 most frequent ingredients, and yields 148 nutritional parameters per mapped ingredient. A Random Forest classifier propagates 34 ingredient categories across the full vocabulary, and a deterministic rule set assigns each recipe a dietary style, making culinary heritage computable for nutritional and geo-cultural analysis.

arXiv:2609.22099v1 Announce Type: new Abstract: 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 recipe's structured ingredient composition, its geo-cultural provenance, and its nutritional profile within a single queryable schema. We address this representation gap by formalizing a framework for recipe data structure that decomposes each recipe into typed ingredient entities, grounds those entities in a reference nutritional database, and annotates them with geo-cultural and dietary context. We present RecipeDB2, a structured compilation of 128,942 recipes with 35,474 ingredients from 32 regions and 99 countries. Ingredient phrases are parsed into seven culinary attributes using a transformer-based named-entity model; ingredients are linked to the USDA reference tables through a BERT embedding strategy F1 = 87.90 on a manually adjudicated set of the 200 most frequent ingredients , yielding 148 nutritional parameters per mapped ingredient; a Random Forest classifier propagates 34 ingredient categories across the full vocabulary; and a deterministic, conservative rule set assigns each recipe a dietary style. Through RecipeDB2 https://cosylab.iiitd.edu.in/recipedb2/ , we demonstrate a scalable framework for making recipes computable, turning culinary heritage long treated as an artistic rather than a quantitative object into a data-driven analysis.