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• W2803135474 abstract "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." @default.
• W2803135474 created "2018-06-01" @default.
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• W2803135474 date "2018-03-21" @default.
• W2803135474 modified "2023-10-10" @default.
• W2803135474 title "Modelling the sensory space of varietal wines: Mining of large, unstructured text data and visualisation of style patterns" @default.
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• W2803135474 doi "https://doi.org/10.1038/s41598-018-23347-w" @default.
• W2803135474 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5862899" @default.
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