Geography and similarity of regional cuisines in China

Food occupies a central position in every culture and it is therefore of great interest to understand the evolution of food culture. The advent of the World Wide Web and online recipe repositories has begun to provide unprecedented opportunities for data-driven, quantitative study of food culture. Here we harness an online database documenting recipes from various Chinese regional cuisines and investigate the similarity of regional cuisines in terms of geography and climate. We found that the geographical proximity, rather than climate proximity is a crucial factor that determines the similarity of regional cuisines. We develop a model of regional cuisine evolution that provides helpful clues to understand the evolution of cuisines and cultures.Comment: 13 pages, 11 figures and 2 table

A neural network system for transformation of regional cuisine style

We propose a novel system which can transform a recipe into any selected regional style (e.g., Japanese, Mediterranean, or Italian). This system has two characteristics. First the system can identify the degree of regional cuisine style mixture of any selected recipe and visualize such regional cuisine style mixtures using barycentric Newton diagrams. Second, the system can suggest ingredient substitutions through an extended word2vec model, such that a recipe becomes more authentic for any selected regional cuisine style. Drawing on a large number of recipes from Yummly, an example shows how the proposed system can transform a traditional Japanese recipe, Sukiyaki, into French style

地理位置对饮食习惯的影响分析

周涛获瑞士弗里堡大学理论物理学博士学位,现为电子科技大学计算机科学与工程学院教授、互联网科学中心主任和大数据研究中心主任。对于绝大部分动物来说,吃东西是首要大事,因为这是获取能量最主要的途径 两千二百多年前,郦食其给刘邦提建议的时候,就特别强调:“王者以民为天,而民以食为天”, 可见吃东西对于人类来说也是第一等的事情。但是,动物吃东西和人吃东西不一样,因为我们不仅要吃进能量,还要吃出花样。所以说,动物没有文化,而人类有文化,而在人类文化中,饮食文化是一个集中的表现。 分析饮食习惯对于我们理解人类文化的形成和变迁是有很大帮助的,特别地,我们希望从不同角度出发,去理解地理位置对文化演进的影响,而这种影响又可以大体上分成两个部分:一是地理相近性所伴随的气候和环境条件的相似性；二是地理相近性带来的交流迁徙的便利性和统治集团的一致性。很多读者能够直观感觉到气候条件和地理位置对于文化的形成、冲突和演进有很重要的影响。本文将介绍如何把这种直观的感受通过大数据分析的办法用量化的方式进行呈现

K-Modes Clustering untuk Mengetahui Jenis Masakan Daerah yang Populer pada Website Resep Online (Studi Kasus: Masakan Banjar di cookpad.com)

AbstrakPada makalah ini dipaparkan clustering pada data resep masakan daerah Banjar untuk mengetahui jenis makanan yang paling banyak di-post secara online oleh pengguna website recipe sharing. Pertama-tama data resep sebanyak 355 dikumpulkan dari suatu website resep, untuk selanjutnya dilakukan ekstraksi data bahan dan pembersihan. Metode clustering yang dipilih adalah k-modes karena cocok digunakan pada data kategorikal. Berdasar metode Elbow, jumlah cluster yang ideal adalah k=4 dan k=8. Jumlah cluster k=4 menghasilkan kelompok yang lebih umum, sedangkan k=8 menghasilkan kelompok yang lebih spesifik. Adapun kelompok yang berhasil diidentifikasi untuk k=4 adalah sayur asam, soto banjar, masakan gurih lain-lain, kue dan bubur manis. Sedangkan kelompok dengan jumlah cluster k=8 adalah sayur asam, soto banjar, kue basah, masakan gurih lain-lain, masak habang, bubur manis, kuah ketupat, dan masakan gurih asam. Evaluasi nilai purity menunjukkan nilai masing-masing 0,825 untuk k=4 dan 0,831 untuk k=8.Kata kunci: data mining, clustering, k-modes, resep masakan, bahanAbstractIn this paper, we cluster user-submitted recipes of Banjar regional cuisine to find out which type of cuisine are popular according to its ingredients. 355 recipes are collected from a recipe sharing website, then the ingredients extracted and cleaned. The clustering method chosen is k-modes because it is suitable for categorical data. Based on the Elbow method, the ideal number of clusters is k = 4 and k = 8. The number of clusters k = 4 produces more general cuisines group, whereas k = 8 produces more specific groups. The groups identified for k = 4 are (1) “sayur asam” (sour soup), (2)“soto banjar” (Banjar chicken soup), (3) savory dishes, and (4) sweet dishes. While the group with the number of clusters k = 8 consists of (1)“sayur asam” (sour soup)  (2) “soto banjar”, (3) Banjar sweet puddings, (4) various savory dishes, (5) “masak habang” (Banjar sweet chili dishes), (6) sweet porridge, (7) “kuah ketupat” (spicy coconut soup) and (8) various savory sour dishes. The purity of clusters are shown to be 0.825 for k=4 and 0.831 for k=8.Keywords: clustering, k-modes, data mining, recipe, ingredien

Coriander (cilantro): a most divisive herb

Coriander is one of the most interesting of herbs/spices given the polarizing (or bivalent) responses that the fresh leaf evokes in people. While many people appreciate the citrusy, herbal qualities of the fresh leaf when added to food, others find its presence to be offensive, describing it as having an unpleasant soapy taste instead. The olfactory receptor (OR) gene responsible for this genetically-determined perceptual difference has now been uncovered, with the incidence of the soapy response estimated at between 3 and 21%, depending on the ethno-cultural group tested. Intriguingly, the spice, coriander seed (actually the dried ripe fruit), does not appear to elicit the same response, hinting at the divergent chemical make-up of the various parts of this popular culinary plant