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A data science study for determining food quality: an application to wine

Year 2019, Volume: 68 Issue: 1, 762 - 770, 01.02.2019
https://doi.org/10.31801/cfsuasmas.469131

Abstract

In this paper, wine quality is investigated based on physicochemical ingredients which include fixed acidity, volatile acidity, citric acid, residual sugar, chloride, free sulfur dioxide, total sulfur dioxide, density, pH, sulphate and alcohol, by ANFIS (Adaptive Neuro Fuzzy Inference System) method and by random forest algorithm which is a powerful classification algorithm. Although this study specifically investigate the relation between physicochemical ingredients and the quality of wine, the results can be adaped to determination of the quality of any food product in terms of the ingredients.

References

  • Jackson, R. S., Wine Science Principles and Applications, 3th Ed. San Diego, USA 2008.
  • Delgado, F.M., Cernadas, E., Barro, S., Do we need hundreds of classifiers to solve real world classification problems?, Journal of Machine Learning Research 15, (2014) 3133-3181.
  • Verma, B. Application of fuzzy logic in post harvest quality decisions. Proceedings of the National Seminar on Post harvest Technology of Fruits. Bangalore, India: University of Agricultural Sciences, 1995.
  • Goel, P., Goel, S., Bhatia, S. Food Quality Assessment Using Fuzzy Logic. 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015.
  • Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., Trystram, G., Fuzzy concepts applied to food quality control: A review, Fuzzy Sets and Systems, 157, (2010) 1145-1154.
  • Askerzade, I.N., Mahmood, M., Control the extension time of traffic light in single junction by using fuzzy logic, International Journal of Electrical & Computer Sciences IJECS--IJENS, 10-2, (2010) 48-55.
  • Ziasabounchi, N., Askerzade, I., ANFIS based classification model for heart disease prediction, International Journal of Electrical & Computer Sciences IJECS--IJENS,14-2, (2014), 7-12.
  • Amrahov, Şahin Emrah, Askerzade, I.N., Strong solutions of the fuzzy linear systems, arXiv preprint arXiv:1107.2126, 2011.
  • Cortez, P., Cerderia, A., Almeida, F., Matos, T., Reis, J., Modelling wine preferences by data mining from physicochemical properties, Decision Support Systems, 47, (2009) 547-533.
  • Negnevitsky, M., A guide to Intelligent Systems, 2nd edition. Edinburg Gate, Harlow.
  • Jang, J.S.R., Sun, C.T., Mizutani, E., Neuro Fuzzy and Soft Computing, Prentice Hall International, Inc. 1997.
  • Askerbeyli, İ., Abbuljabar, J.S., Using fuzzy logic methods for carbon dioxide control in carbonated beverages. International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11-3, (2011), 196-202.
Year 2019, Volume: 68 Issue: 1, 762 - 770, 01.02.2019
https://doi.org/10.31801/cfsuasmas.469131

Abstract

References

  • Jackson, R. S., Wine Science Principles and Applications, 3th Ed. San Diego, USA 2008.
  • Delgado, F.M., Cernadas, E., Barro, S., Do we need hundreds of classifiers to solve real world classification problems?, Journal of Machine Learning Research 15, (2014) 3133-3181.
  • Verma, B. Application of fuzzy logic in post harvest quality decisions. Proceedings of the National Seminar on Post harvest Technology of Fruits. Bangalore, India: University of Agricultural Sciences, 1995.
  • Goel, P., Goel, S., Bhatia, S. Food Quality Assessment Using Fuzzy Logic. 2nd International Conference on Computing for Sustainable Global Development (INDIACom), 2015.
  • Perrot, N., Ioannou, I., Allais, I., Curt, C., Hossenlopp, J., Trystram, G., Fuzzy concepts applied to food quality control: A review, Fuzzy Sets and Systems, 157, (2010) 1145-1154.
  • Askerzade, I.N., Mahmood, M., Control the extension time of traffic light in single junction by using fuzzy logic, International Journal of Electrical & Computer Sciences IJECS--IJENS, 10-2, (2010) 48-55.
  • Ziasabounchi, N., Askerzade, I., ANFIS based classification model for heart disease prediction, International Journal of Electrical & Computer Sciences IJECS--IJENS,14-2, (2014), 7-12.
  • Amrahov, Şahin Emrah, Askerzade, I.N., Strong solutions of the fuzzy linear systems, arXiv preprint arXiv:1107.2126, 2011.
  • Cortez, P., Cerderia, A., Almeida, F., Matos, T., Reis, J., Modelling wine preferences by data mining from physicochemical properties, Decision Support Systems, 47, (2009) 547-533.
  • Negnevitsky, M., A guide to Intelligent Systems, 2nd edition. Edinburg Gate, Harlow.
  • Jang, J.S.R., Sun, C.T., Mizutani, E., Neuro Fuzzy and Soft Computing, Prentice Hall International, Inc. 1997.
  • Askerbeyli, İ., Abbuljabar, J.S., Using fuzzy logic methods for carbon dioxide control in carbonated beverages. International Journal of Electrical & Computer Sciences IJECS-IJENS Vol: 11-3, (2011), 196-202.
There are 12 citations in total.

Details

Primary Language English
Subjects Mathematical Sciences
Journal Section Review Articles
Authors

Ata Emre Ozalp This is me 0000-0002-9170-3013

İman Askerzade This is me 0000-0003-4466-8128

Publication Date February 1, 2019
Submission Date November 23, 2017
Acceptance Date April 30, 2018
Published in Issue Year 2019 Volume: 68 Issue: 1

Cite

APA Ozalp, A. E., & Askerzade, İ. (2019). A data science study for determining food quality: an application to wine. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 68(1), 762-770. https://doi.org/10.31801/cfsuasmas.469131
AMA Ozalp AE, Askerzade İ. A data science study for determining food quality: an application to wine. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. February 2019;68(1):762-770. doi:10.31801/cfsuasmas.469131
Chicago Ozalp, Ata Emre, and İman Askerzade. “A Data Science Study for Determining Food Quality: An Application to Wine”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68, no. 1 (February 2019): 762-70. https://doi.org/10.31801/cfsuasmas.469131.
EndNote Ozalp AE, Askerzade İ (February 1, 2019) A data science study for determining food quality: an application to wine. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68 1 762–770.
IEEE A. E. Ozalp and İ. Askerzade, “A data science study for determining food quality: an application to wine”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 68, no. 1, pp. 762–770, 2019, doi: 10.31801/cfsuasmas.469131.
ISNAD Ozalp, Ata Emre - Askerzade, İman. “A Data Science Study for Determining Food Quality: An Application to Wine”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 68/1 (February 2019), 762-770. https://doi.org/10.31801/cfsuasmas.469131.
JAMA Ozalp AE, Askerzade İ. A data science study for determining food quality: an application to wine. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2019;68:762–770.
MLA Ozalp, Ata Emre and İman Askerzade. “A Data Science Study for Determining Food Quality: An Application to Wine”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 68, no. 1, 2019, pp. 762-70, doi:10.31801/cfsuasmas.469131.
Vancouver Ozalp AE, Askerzade İ. A data science study for determining food quality: an application to wine. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2019;68(1):762-70.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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