Modeling wine preferences by data mining from physicochemical properties

  • Authors:
  • Paulo Cortez;António Cerdeira;Fernando Almeida;Telmo Matos;José Reis

  • Affiliations:
  • Department of Information Systems/R&D Centre Algoritmi, University of Minho, 4800-058 Guimarãães, Portugal;Viticulture Commission of the Vinho Verde Region (CVRVV), 4050-501 Porto, Portugal;Viticulture Commission of the Vinho Verde Region (CVRVV), 4050-501 Porto, Portugal;Viticulture Commission of the Vinho Verde Region (CVRVV), 4050-501 Porto, Portugal;Department of Information Systems/R&D Centre Algoritmi, University of Minho, 4800-058 Guimarãães, Portugal and Viticulture Commission of the Vinho Verde Region (CVRVV), 4050-501 Porto, P ...

  • Venue:
  • Decision Support Systems
  • Year:
  • 2009

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Abstract

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.