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Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data

机译:在中世纪欧洲烹饪中的味道配对:用肮脏数据烹饪的研究

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An important part of cooking with computers is using statistical methods to create new, flavorful ingredient combinations. The flavor pairing hypothesis states that culinary ingredients with common chemical flavor components combine well to produce pleasant dishes. It has been recently shown that this design principle is a basis for modern Western cuisine and is reversed for Asian cuisine. Such data-driven analysis compares the chemistry of ingredients to ingredient sets found in recipes. However, analytics-based generation of novel flavor profiles can only be as good as the underlying chemical and recipe data. Incomplete, inaccurate, and irrelevant data may degrade flavor pairing inferences. Chemical data on flavor compounds is incomplete due to the nature of the experiments that must be conducted to obtain it. Recipe data may have issues due to text parsing errors, imprecision in textual descriptions of ingredients, and the fact that the same ingredient may be known by different names in different recipes. Moreover, the process of matching ingredients in chemical data and recipe data may be fraught with mistakes. Much of the 'dirtiness' of the data cannot be cleansed even with manual curation. In this work, we collect a new data set of recipes from Medieval Europe before the Columbian Exchange and investigate the flavor pairing hypothesis historically. To investigate the role of data incompleteness and error as part of this hypothesis testing, we use two separate chemical compound data sets with different levels of cleanliness. Notably, the different data sets give conflicting conclusions about the flavor pairing hypothesis in Medieval Europe. As a contribution towards social science, we obtain inferences about the evolution of culinary arts when many new ingredients are suddenly made available. are usually, but not exclusively, applied in domains historically associated with creative people, such as mathematics and science, poetry and story telling, musical composition and performance, video game, architectural, industrial and graphic design, the visual, and even the culinary, arts" [Colton and Wiggins, 2012].
机译:烹饪与计算机的重要组成部分是使用统计方法来创造新的美味成分组合。味道配对假设指出,具有普通化学品风味部件的烹饪成分结合了生产宜人的菜肴。最近据表明,这种设计原则是现代西式美食的基础,并为亚洲美食逆转。这种数据驱动分析将成分的化学与食谱中发现的成分集进行了比较。然而,基于分析的新颖风味简介的产生只能与底层化学和配方数据一样好。不完全,不准确,无关紧要的数据可能降低风味配对推论。由于必须进行的实验性质,风味化合物的化学数据是不完整的。食谱数据可能由于文本解析错误而产生的问题,在成分的文本描述中的不精确,以及相同的成分可以通过不同的配方中的不同名称所知的事实。此外,可以通过错误充满困境的化学数据和配方数据中成分的过程。即使使用手动策策,也不能清洁数据的大部分“肮脏”。在这项工作中,我们在哥伦比亚交流之前从中世纪欧洲收集一组新的数据集,并在历史上调查了风味配对假设。为了调查数据不完整和误差作为本假设检测的一部分的作用,我们使用两组单独的化合物数据集,具有不同的清洁度。值得注意的是,不同的数据集对中世纪欧洲的风味配对假设的结论产生了矛盾的结论。作为对社会科学的贡献,当许多新的成分突然可用时,我们获得了关于烹饪艺术的演变的推论。通常但不是完全,在历史上与创造性的人相关联的域名,如数学和科学,诗歌和故事,音乐作品和表演,视频游戏,建筑,工业和平面设计,视觉,甚至是烹饪,艺术“[Colton和Wiggins,2012]。

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