  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.
