Data analysis in the kitchen: Modeling flavor networks to predict tasty ingredient combinations

What’s on the menu tonight? How about some roast beef with strawberry-, beer- and garlic sauce? Or perhaps something lighter based on tomatoes, apricots and whiskey gum? Gourmet chefs and foodies alike love to experiment in the kitchen and come up with new flavor combinations, and recent research is taking the science of combining ingredients to a whole new—computable—level. New research published in Frontiers in ICT suggests and analyses a possible new principle behind ingredient mixing in traditional cuisines—the food-bridging hypothesis – and compares it to the previously suggested food-pairing hypothesis, in order to examine what data driven graphical modelling can tell us about tasty ingredient combinations.