Food-Bridging: A New Network Construction to Unveil the Principles of Cooking

Abstract

In this manuscript, we propose, analyze, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. (2011). The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavor by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid foodpairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.

Data Mining a Medieval Medical Text Reveals Patterns in Ingredient Choice That Reflect Biological Activity against Infectious Agents

“…To normalize the link weights, we used a linear transformation that did not affect the network properties (102,103). More specifically, if nodes i and j were connected, their link weight (W i,j new ) transformed as…”

Utilization of text mining as a big data analysis tool for food science and nutrition

“…The examples of applying network analysis in food domain can be found in Section 4. For example, in Subsection 4.4, a flavor network was constructed based on online recipes and analyzed for providing ideas of creative cooking (Ahn, Ahnert, Bagrow, & Barabási, 2011;Simas, Ficek, Diaz-Guilera, Obrador, & Rodriguez, 2017). In Subsection 4.5, food disease and food nutrient networks were build based on literature text mining (Jensen et al, 2014;Karaa et al, 2016;Kim, Sung, Foo, Jin, & Kim, 2015;Yang et al, 2011).…”

Construction, comparison and evolution of networks in life sciences and other disciplines

“…Understanding how compounds inside each ingredient can be paired in a recipe using data science is called computational gastronomy [309]. Food-pairing networks can be analysed by treating ingredients as nodes and their co-occurrence in recipes as weighted links [310]. More specifically, the link's weight is given by the number of flavour compounds two nodes (e.g.…”