Quality assessment is a crucial issue within the wine industry. The traditional way of assessing by human experts is time consuming and very expensive. Machine learning techniques help in the process of quality assurance in a wide range of industries. The purpose of this study was to develop and offer a free software, for winemakers and customers in which they can easily provide the physicochemical properties of the wine and receive an accurate prediction of the anticipated quality and type of the wine. We used comprehensive datasets of 6497 examples, which contained physicochemical properties and appropriate quality. We combined these datasets, built and trained several neural networks models. We evaluated their performance and selected the best model. Wine quality estimations were modeled as a regression problem and wine type detection as a classification problem. The best model performed well for prediction of wine quality (root means squared error = 0.54) and type (f-score=0.99). With our free software, winemakers and customers can examine how a fine change in each physicochemical property could affect the quality of the wine. They could easily figure out the importance of each physicochemical property, and which one to ignore for reduction of cost. The process is very fast, accurate and does not require taste experts for sensory tests.
As a subfield of artificial intelligence, machine learning designed to learn the structure of the data. Machine learning has been widely used in many scientific problems. In this study, we used machine learning techniques to figure out the most important physicochemical properties for type classification of red wines. We used a wines' dataset with 13 physicochemical properties. We used a Random Forest classifier to predict wine's type from its features, and permutation feature importance, in order to detect the most important properties of the wine for type classification. The properties: flavanoids, proline, and color intensity were found to be most important for type classification. Additional 4 classifiers: Laso classifier, Ridge classifier, Decision Tree classifier, and Support Vector classifier were used and examined for classification and feature importance. Flavanoids and proline were very important across all classifiers.
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