The Classification of White Wine and Red Wine According to Their Physicochemical Qualities

Abstract

The main purpose of this study is to predict wine quality based on physicochemical data. In this study, two large separate data sets which were taken from UC Irvine Machine Learning Repository were used. These data sets contain 1599 instances for red wine and 4898 instances for white wine with 11 features of physicochemical data such as alcohol, chlorides, density, total sulfur dioxide, free sulfur dioxide, residual sugar, and pH. First, the instances were successfully classified as red wine and white wine with the accuracy of 99.5229% by using Random Forests Algorithm. Then, the following three different data mining algorithms were used to classify the quality of both red wine and white wine: k-nearest-neighbourhood, random forests and support vector machines. There are 6 quality classes of red wine and 7 quality classes of white wine. The most successful classification was obtained by using Random Forests Algorithm. In this study, it is also observed that the use of principal component analysis in the feature selection increases the success rate of classification in Random Forests Algorithm.

Keywords

• Classification,Random Forests,Support Vector Machines,k Nearest Neighbourhood

References

1. P. Cortez, A. Cerderia, F. Almeida, T. Matos, and J. Reis, “Modelling wine preferences by data mining from physicochemical properties,” In Decision Support Systems, Elsevier, 47 (4): 547-553. ISSN: 0167-9236.
2. S. Ebeler, “Linking Flavour Chemistry to Sensory Analysis of Wine,” in Flavor Chemistry, Thirty Years of Progress, Kluwer Academic Publishers, 1999, pp. 409-422.
3. V. Preedy, and M. L. R. Mendez, “Wine Applications with Electronic Noses,” in Electronic Noses and Tongues in Food Science, Cambridge, MA, USA: Academic Press, 2016, pp. 137-151.
4. A. Asuncion, and D. Newman (2007), UCI Machine Learning Repository, University of California, Irvine, [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html
5. S. Kallithraka, IS. Arvanitoyannis, P. Kefalas, A. El-Zajouli, E. Soufleros, and E. Psarra, “Instrumental and sensory analysis of Greek wines; implementation of principal component analysis (PCA) for classification according to geographical origin,” Food Chemistry, 73(4): 501-514, 2001.
6. N. H. Beltran, M. A. Duarte- MErmound, V. A. S. Vicencio, S. A. Salah, and M. A. Bustos, “Chilean wine classification using volatile organic compounds data obtained with a fast GC analyzer,” Instrum. Measurement, IEEE Trans., 57: 2421-2436, 2008.
7. S. Shanmuganathan, P. Sallis, and A. Narayanan, “Data mining techniques for modelling seasonal climate effects on grapevine yield and wine quality,” IEEE International Conference on Computational Intelligence Communication Systems and Networks, pp. 82-89, July 2010.
8. B. Chen, C. Rhodes, A. Crawford, and L. Hambuchen, “Wineinformatics: applying data mining on wine sensory reviews processed by the computational wine wheel,” IEEE International Conference on Data Mining Workshop, pp. 142-149, Dec. 2014.

9. UCI Machine Learning Repository, Wine quality data set, [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Wine+Quality.
10. J. Han, M. Kamber, and J. Pei, “Classification: Basic Concepts,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 327-393.
11. J. Han, M. Kamber, and J. Pei, “Classification: Advanced Methods,” in Data Mining Concepts and Techniques, 3rd ed., Waltham, MA, USA: Morgan Kaufmann, 2012, pp. 393-443.
12. W. L. Martinez, A. R. Martinez, “Supervised Learning” in Computational Statistics Handbook with MATLAB, 2nd ed., Boca Raton, FL, USA: Chapman & Hall/CRC, 2007, pp. 363-431.