A topological analysis of the space of recipes

TL;DR

This paper applies topological data analysis, specifically persistent homology, to explore the structure of culinary recipes and proposes a method to generate novel ingredient combinations, validated through sensory evaluation.

Contribution

It introduces the use of topological data analysis in culinary research and develops a new method for creating innovative recipes based on topological features.

Findings

Topological analysis reveals multiscale 'holes' in recipe space.

Generated recipes with novel ingredient combinations are acceptable in sensory tests.

Topological methods offer new insights for culinary innovation.

Abstract

In recent years, the use of data-driven methods has provided insights into underlying patterns and principles behind culinary recipes. In this exploratory work, we introduce the use of topological data analysis, especially persistent homology, in order to study the space of culinary recipes. In particular, persistent homology analysis provides a set of recipes surrounding the multiscale "holes" in the space of existing recipes. We then propose a method to generate novel ingredient combinations using combinatorial optimization on this topological information. We made biscuits using the novel ingredient combinations, which were confirmed to be acceptable enough by a sensory evaluation study. Our findings indicate that topological data analysis has the potential for providing new tools and insights in the study of culinary recipes.