Regional cuisines often differ substantially in their cooking methods, their food preparation and above all their ingredients. But they can also be closely related. So here’s an interesting question: what factors determine the links between regional cuisines?
One possibility is that climate is the primary factor. Similar ingredients grow in similar climates and climate also drives the use of certain spices that act as preservatives.
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Another option is that geographical proximity is the primary factor in cuisine similarity. The thinking here is that people are more likely to travel between geographically close regions and take their cuisines with them.
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Settling this question is not easy. But in recent years a new tool has become available for analysing the nature and recipes, ingredients they contain and how this varies from one place to another. This tool is the World Wide Web and a huge databases it contains recipe websites around the world.
Today, Yu-Xiao Zhu at the Beijing Computational Science Research Center in China and a few buddies use a web-based recipe database to analyse the cuisines from 20 regions in China. They then compared these cuisines and worked out whether geography or climate was the more important factor in the similarities.
They began by downloading all the recipes from a Chinese recipe website called Meishijie. This contained almost 8500 recipes based on nearly 3000 ingredients. They grouped the recipes according to their origin in one of 20 regions.
Finally they created a food web consisting of the set of all recipes on the set of all ingredients. Where recipe contains an ingredient they draw a link between them. Since each recipe belongs to anyone regional cuisines these links can then also be categorised into cuisines. Counting these links shows how prevalent each ingredient is in each cuisine.
In the next step, Yu-Xiao and co developed a way of measuring similarities between cuisines. Of course, some ingredients such as salt, sugar and egg appear in a large fraction recipes in all cuisines so these give little information about similarities. Instead, Yu-Xiao and co concentrated on less common ingredients to see how these are distributed.
The results throw up a couple of surprises. First they found two regional cuisines that differed substantially from each other and from everything else. These are the cuisines associated with Hong Kong and YunGui. “This may reflect the facts that ethnic minorities have historically resided in the YunGui region and that Hong Kong was ruled by the British Empire and Japan for more than 100 years,” say the team.
They then looked at the similarities between cuisines from similar climates as measured by their average temperature difference. They also looked at the similarities between cuisines that are geographically close to each other (while also allowing for the fact that they may also have similar climates).
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In some ways, their conclusion is unsurprising. “We found that the geographical proximity, rather than climate proximity is a crucial factor that determines the similarity of regional cuisines,” they say.
That also provides an interesting insight into the way food cultures evolve. Clearly, people move from one region to another, taking their recipes with them, where they can modify them as they wish. Obviously, that happens more often between regions are geographically close.
Yu-Xiao and co even greater computer model of this process. The novel begins with a regional cuisine that consists of a certain number of recipes ingredients. Certain recipe can appear in another region the probability that is proportional to the distance. When this happens, one new ingredient is added to the recipe and one old ingredient taken away.
Yu-Xiao and co show that as this model involves it produces a distribution of cuisines that is remarkably similar to that seen in their database. In other words the is a good model of the real-life evolution of food culture.
This work is just one aspect of the emerging field of food network science. We’ve looked at this topic on this blog in the past and there is no question that we are likely to see more lip-smacking insights in the coming months and years.
In particular, it would be interesting to see how migration influences the food networks around the world on a much larger scale and whether they can be linked to other patterns such as trade networks and so on. Much food for thought (ahem).
