Using Data Mining for Wine Quality Assessment

Authors:
Paulo Cortez;Juliana Teixeira;António Cerdeira;Fernando Almeida;Telmo Matos;José Reis
Affiliations:
Dep. of Information Systems/Algoritmi Centre, University of Minho, Guimarães, Portugal 4800-058;Dep. of Information Systems/Algoritmi Centre, University of Minho, Guimarães, Portugal 4800-058;Viticulture Commission of the Vinho Verde region (CVRVV), Porto, Portugal 4050-501;Viticulture Commission of the Vinho Verde region (CVRVV), Porto, Portugal 4050-501;Viticulture Commission of the Vinho Verde region (CVRVV), Porto, Portugal 4050-501;Dep. of Information Systems/Algoritmi Centre, University of Minho, Guimarães, Portugal 4800-058 and Viticulture Commission of the Vinho Verde region (CVRVV), Porto, Portugal 4050-501
Venue:
DS '09 Proceedings of the 12th International Conference on Discovery Science
Year:
2009

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Abstract

Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physicochemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certification step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a regression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful for understanding how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.