2013/07/30 by Kush R. Varshney, Varshney, Kush R., Lav R. Varshney +6 · 1 citation
Agricultural and Biological Sciences · Business, Management and Accounting · Computer Science · Physics and Astronomy · #Computers and Society (cs.CY) #Culinary Culture and Tourism #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Fermentation and Sensory Analysis #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #Wine Industry and Tourism #cs.CY #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1307.7982
IJCAI
arxiv created 2013/07/30 · openalex publication_date 2013/07/30 · arxiv updated 2013/07/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.