Abstract
The increasingly large volumes of publicly available sensory descriptions of wine raises the question whether this source of data can be mined to extract meaningful domain-specific information about the sensory properties of wine. We introduce a novel application of formal concept lattices, in combination with traditional statistical tests, to visualise the sensory attributes of a big data set of some 7,000 Chenin blanc and Sauvignon blanc wines. Complexity was identified as an important driver of style in hereto uncharacterised Chenin blanc, and the sensory cues for specific styles were identified. This is the first study to apply these methods for the purpose of identifying styles within varietal wines. More generally, our interactive data visualisation and mining driven approach opens up new investigations towards better understanding of the complex field of sensory science.
Highlights
Recent years have seen the emergence of rapidly growing volumes of publicly available information on the sensory properties of foodstuffs[1,2,3,4]
Earlier work proposing the application of concept lattices to knowledge discovery in databases (KDD) established the basis for interactive data visualisation/ mining used in further sections of this paper[12,13]
The sensory descriptors included in this study were used by the Platter’s panellists to describe all the Chenin blanc and Sauvignon blanc wines entered into the Platter Guide from 2008 to 2017
Summary
Recent years have seen the emergence of rapidly growing volumes of publicly available information on the sensory properties of foodstuffs[1,2,3,4]. We took a novel approach and mined the Platter’s data for more than 2,500 Chenin blanc wines that were produced over a 7-year period, 2008–2014.
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