Nuriel Shalom Mor, Tigabo Asras, Eli Gal, Tesfahon Demasia, Ezra Tarab, Nathaniel Ezekiel, Osher Nikapros, Oshri Semimufar, Eva Gladky, Maria Karpenko, Daniel Sason, Д. Н. Маслов, Omri Mo. Wine quality and type prediction from physicochemical properties using neural networks for machine learning: a free software for winemakers and customers.. 2022. agriXiv.2022.00125
Wine quality and type prediction from physicochemical properties using neural networks for machine learning: a free software for winemakers and customers.
DOI: 10.31220/agriRxiv.2022.00125
Statement: This article is a preprint and has not been peer-reviewed. It reports new research that has yet to be evaluated and so should not be used to guide clinical practice.
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Abstract: Abstract 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.
Key words: wine; type; wine quality; winemakers; physicochemical properties; customerSubmit time: 1 January 2022
Copyright: The copyright holder for this preprint is the author/funder, who has granted agriXiv a license to display the preprint in perpetuity. -
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