Discovering novel ingredient pairings in molecular gastronomy using network analysis

TL;DR

This paper applies network analysis to molecular gastronomy, enabling the discovery of novel ingredient pairings by analyzing molecular structures, thus facilitating innovative recipe creation.

Contribution

It introduces a network science approach to identify compatible ingredient pairings based on molecular data, which is a novel method in culinary research.

Findings

Generated a set of compatible ingredients for new recipes

Demonstrated the effectiveness of network analysis in flavor pairing

Simplified the process of creating innovative ingredient combinations

Abstract

Molecular gastronomy is a distinct sub-discipline of food science that takes an active role in examining chemical and physical properties of ingredients and as such lends itself to more scientific approaches to finding novel ingredient pairings. With thousands of ingredients and molecules, which participate in the creation of each ingredient's flavour, it can be difficult to find compatible flavours in an efficient manner. Existing literature is focused mainly on analysis of already established cuisine based on the flavour profile of its ingredients, but fails to consider the potential in finding flavour compatibility for use in creation of completely new recipes. Expressing relationships between ingredients and their molecular structure as a bipartite network opens up this problem to effective analysis with methods from network science. We describe a series of experiments on a database of food using network analysis, which produce a set of compatible ingredients that can be used in creation of new recipes. We expect this approach and its results to dramatically simplify the creation of new recipes with previously unseen and fresh combinations of ingredients.