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Predicting Wine Score based on Physicochemical Properties

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DOI: 10.23977/afshn.2022.040101 | Downloads: 32 | Views: 1478

Author(s)

Congrui Li 1

Affiliation(s)

1 School of Statistics, Beijing Normal University, Beijing, 100875, China

Corresponding Author

Congrui Li

ABSTRACT

Existing wine scoring systems are based on subjective feelings, which are difficult to quantify. Thus, the score of a particular wine has little guidance on the brewing process. In this paper, regression tree and random forest are used to predict wine score by using the physicochemical properties of wine. According to the results of the model, higher quality wine can be produced by controlling the physical and chemical properties, thus reducing the waste of raw materials.

KEYWORDS

Random Forest, Regression Tree, Wine Score, physicochemical properties

CITE THIS PAPER

Congrui Li, Predicting Wine Score based on Physicochemical Properties. Advances in Food Science and Human Nutrition (2022) Vol. 4: 1-5. DOI: http://dx.doi.org/10.23977/afshn.2022.040101.

REFERENCES

[1] Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
[2]P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
[3] https://www.wine-world.com/culture/pj/20150605173918670

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