Selection of features and prediction of wine quality using artificial neural networks

Abstract

The assessment of wine taste quality is a key factor for successful sales in the wine industry, where the aim is to fulfill the consumer's needs. Usually, this is determined by human experts who make the evaluation process very expensive and time-consuming. This study intends to introduce an alternative method for the prediction of wine quality with the usage of machine learning techniques such as linear regression and neural networks. Our data analysis is based on a real wine dataset provided by an established winery in Greece. First of all, we determine the dependence of the quality from selected physicochemical features of wine. We use some well-known algorithms to achieve better results in statistical calculations and specific methods of selecting the best possible number of variables using principal component analysis (PCA) and linear regression. After using artificial neural networks and checking various combinations of layers we conclude how the proposed statistical techniques improve the accuracy of the prediction of the wine quality using the previously selected features.