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Article

Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection

1
Institute of Informatics, Federal University of Goiás, 74690-900 Goiânia-Go, Brazil
2
LADAF—Laboratory of Functional Foods, Department of Food and Experimental Nutrition, Faculty of Pharmaceutical Sciences, University of São Paulo, Av. Lineu Prestes 580, B14, 05508-900 São Paulo, Brazil
*
Author to whom correspondence should be addressed.
Beverages 2018, 4(4), 97; https://doi.org/10.3390/beverages4040097
Received: 30 September 2018 / Revised: 10 November 2018 / Accepted: 27 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Wine Traceability )
Geographical product recognition has become an issue for researchers and food industries. One way to obtain useful information about the fingerprint of wines is by examining that fingerprint’s chemical components. In this paper, we present a data mining and predictive analysis to classify Brazilian and Uruguayan Tannat wines from the South region using the support vector machine (SVM) classification algorithm with the radial basis kernel function and the F-score feature selection method. A total of 37 Tannat wines differing in geographical origin (9 Brazilian samples and 28 Uruguayan samples) were analyzed. We concluded that given the use of at least one anthocyanin (peon-3-glu) and the radical scavenging activity (DPPH), the Tannat wines can be classified with 94.64% accuracy and 0.90 Matthew’s correlation coefficient (MCC). Furthermore, the combination of SVM and feature selection proved useful for determining the main chemical parameters that discriminate with regard to the origin of Tannat wines and classifying them with a high degree of accuracy. Additionally, to our knowledge, this is the first study to classify the Tannat wine variety in the context of two countries in South America. View Full-Text
Keywords: support vector machines; data mining; wine classification; Tannat wines; feature selection support vector machines; data mining; wine classification; Tannat wines; feature selection
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MDPI and ACS Style

Costa, N.L.; Llobodanin, L.A.G.; Castro, I.A.; Barbosa, R. Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection. Beverages 2018, 4, 97. https://doi.org/10.3390/beverages4040097

AMA Style

Costa NL, Llobodanin LAG, Castro IA, Barbosa R. Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection. Beverages. 2018; 4(4):97. https://doi.org/10.3390/beverages4040097

Chicago/Turabian Style

Costa, Nattane Luíza, Laura Andrea García Llobodanin, Inar Alves Castro, and Rommel Barbosa. 2018. "Geographical Classification of Tannat Wines Based on Support Vector Machines and Feature Selection" Beverages 4, no. 4: 97. https://doi.org/10.3390/beverages4040097

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