By K. R. Varshney, L. R. Varshney, J. Wang, and D. Meyers
Proceedings of the International Joint Conference on Artificial Intelligence Workshops, Beijing, China (2013)
Abstract: 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.