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Modeling the Preference of Wine Quality Using Logistic Regression Techniques Based on Physicochemical Properties

Agyemang, Perpetual O.

Abstract Details

2010, Master of Science in Mathematics, Youngstown State University, Department of Mathematics and Statistics.

Wine quality is attributed to many different factors of the wine working collectively to bear a sensory experience that is not apparent from considering these components in isolation. The various chemical components in wine give the wine its distinct taste and aroma. Appreciation of wine quality involves moving beyond our innate preferences. Currently, about 1300 components relating to wine quality have been identified in wine and new components continue to be found. These physicochemical properties can be used to model wine quality.

This review presents an analysis to extend what P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis accomplished using support vector machine and neural network methods for modeling wine preferences by data mining from physicochemical properties. Two logistic regression approaches are used to predict human wine taste preferences with the goal of better predictions. The data were subject to the logistic regression analysis to develop suitable equations to predict which components were significant in the determination of quality of wine. Since ordering exists in the dependent variable, we first considered using ordinal logistic regression. Ordinal logistic regression is a statistical technique whose dependent variable is the order response category variable and the independent variables may be categorical, interval or ration scale. An order response variable is useful for subjective assessment of quality, importance or relevance. After applying this technique, we realized that sulphate, which improves the scent of wine, and citric acid were significant as an indication of quality in both red and white wine. As some of the assumptions of the ordinal logistic model were violated, we employed multinomial logistic regression as well. Multinomial logistic regression is used when the dependent (response) variable in question is nominal, i.e. a set of categories which cannot be ordered in any meaningful way (for example, societal class) and consists of more than two categories. It assumes that data are case specific (each independent variable has a single value for each case), independent of inappropriate options. Using this technique, alcohol was statistically significant and had a negative effect throughout the various quality levels of red wine. pH was statistically significant and had a negative effect throughout the various quality levels of white wine.

This research provides a useful basis for assessing the various chemical components in wine that give wine its quality, using two regression approaches. The model built for wine quality in this analysis is anticipated to be of great use because of its dependence on only seven components for red wine and eight components for white wine.

Thomas Wakefield, PhD (Advisor)
G. Andy Chang, PhD (Committee Member)
G. Jay Kerns, PhD (Committee Member)
42 p.

Recommended Citations

Citations

  • Agyemang, P. O. (2010). Modeling the Preference of Wine Quality Using Logistic Regression Techniques Based on Physicochemical Properties [Master's thesis, Youngstown State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1298055470

    APA Style (7th edition)

  • Agyemang, Perpetual. Modeling the Preference of Wine Quality Using Logistic Regression Techniques Based on Physicochemical Properties. 2010. Youngstown State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ysu1298055470.

    MLA Style (8th edition)

  • Agyemang, Perpetual. "Modeling the Preference of Wine Quality Using Logistic Regression Techniques Based on Physicochemical Properties." Master's thesis, Youngstown State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1298055470

    Chicago Manual of Style (17th edition)