Work

Exploring Predictive Modeling for Food Quality Enhancement: A Case Study on Wine

Year: 2025

Type: article

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.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc9030055

Authors: Cemil Yavas, Jongyeop Kim, Lei Chen, Christopher Kadlec, Yiming Ji

Institutions: Georgia Southern University, Kennesaw State University

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funder: National Science Foundation