  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.
