A Computational Gastronomy Framework for Achieving Better Nutrition and Public Health

Context & Background

Dietary habits are directly linked to public health issues like diabetes, cardiovascular diseases, and malnutrition. Computational Gastronomy is an interdisciplinary science that uses data analysis to study food, recipes, chemical flavor profiles, and health outcomes to automate the creation of healthy, balanced diets.

Problems to be Addressed

Culinary data (recipes, ingredients) is fragmented and unstandardized. There are no robust tools to predict the nutritional values of custom regional recipes or generate new recipes that maintain taste while optimizing for health.

Aims and Objectives

1. Compile a structured library of Indian recipes and ingredient values.
2. Create a database of flavor compounds in natural ingredients.
3. Design algorithms for recipe generation and nutrition estimation.

Methodology

The team uses NLP (Named Entity Recognition) to extract ingredients and quantities from raw recipe texts. These are mapped to nutritional databases. Machine learning models analyze flavor pairing rules to generate novel recipes, creating a 'Turing Test for Chefs' to validate taste and health profiles.

Expected Outcomes

FlavorDB2 database update, a web portal for recipe generation (Ratatouille), and computational gastronomy frameworks adopted by health and hospitality sectors.