Wine Quality and Type Prediction from Physicochemical Properties Using Neural Networks for Machine Learning: A Free Software for Winemakers and Customers

Nuriel Shalom Mor, Tigabo Asras, Eli Gal, Tesfahon Demasia, Ezra Tarab, Nathaniel Ezekiel, Osher Nikapros, Oshri Semimufar, Eva Gladky and Maria Karpenko

No ph4cu_v1, MetaArXiv from Center for Open Science

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 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 the 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.

Date: 2022-02-01
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Persistent link: https://EconPapers.repec.org/RePEc:osf:metaar:ph4cu_v1

DOI: 10.31219/osf.io/ph4cu_v1

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