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TitleUsing data mining for wine quality assessment
Author(s)Cortez, Paulo
Teixeira, Juliana
Cerdeira, António
Almeida, Fernando
Matos, Telmo
Reis, José
KeywordsOrdinal regression
Sensitivity analysis
Sensory preferences
Support vector machines
Variable and model selection
Wine science
Issue date2009
JournalLecture Notes in Computer Science
CitationGAMA, João [et al.], ed. lit. – “Discovery science : proceedings of the International Conference, DS 2009, 12, Porto, Portugal, 2009”. Berlin : Springer, cop. 2009. (Lecture Notes in Artificial Intelligence ; vol. 5808). ISBN 3-642-04746-7. p. 66-69.
Abstract(s)Certification and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physico- chemical (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 certifi- cation step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a re- gression 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 do- main. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selec- tion and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regres- sion and neural network methods. Such model is useful for understand- ing how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings
DSI - Engenharia da Programação e dos Sistemas Informáticos

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