Information Technology: Faculty Publications

Document Type

Article

Publication Date

2-26-2025

Publication Title

Big Data and Cognitive Computing

DOI

10.3390/bdcc9030055

Abstract

What makes a wine exceptional enough to score a perfect 10 from experts? This study explores a data-driven approach to identify the ideal physicochemical composition for wines that could achieve this highest possible rating. Using a dataset of 11 measurable attributes, including alcohol, sulfates, residual sugar, density, and citric acid, for wines rated up to a maximum quality score of 8 by expert tasters, we sought to predict compositions that might enhance wine quality beyond current observations. Our methodology applies a second-degree polynomial ridge regression model, optimized through an exhaustive evaluation of feature combinations. Furthermore, we propose a specific chemical and physical composition of wine that our model predicts could achieve a quality score of 10 from experts. While further validation with winemakers and industry experts is necessary, this study aims to contribute a practical tool for guiding quality exploration and advancing predictive modeling applications in food and beverage sciences.

Comments

Georgia Southern University faculty members, Jongyeop Kim, Lei Chen, Christopher Kadlec, and Yiming Ji co-authored "Exploring Predictive Modeling for Food Quality Enhancement: A Case Study on Wine".

Copyright

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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