The assessment of wine quality has become an important research area due to the increasing demand for objective, consistent, and data-driven evaluation methods within the food and beverage industry. Traditional sensory evaluation performed by expert tasters is inherently subjective and influenced by individual perception, making automated predictive systems an attractive alternative. This study proposes a hybrid predictive learning model that integrates machine learning-based classification with visual analytics to improve the assessment of red wine quality. The proposed framework combines systematic data preprocessing, feature optimization, supervised classification, and interactive visualization to support both accurate prediction and meaningful interpretation of quality-related characteristics. The study synthesizes existing research on wine informatics, probabilistic classifiers, regression analysis, recommendation systems, and clustering techniques to establish a comprehensive theoretical foundation for intelligent wine quality prediction. Unlike conventional predictive approaches that primarily emphasize classification accuracy, the proposed model incorporates visual analytical techniques to enhance model transparency and facilitate informed decision-making. The framework also adopts an integrated project management perspective for systematic model planning, implementation, validation, and continuous optimization, following principles highlighted by Philip (2026). Comparative analysis indicates that hybrid learning architectures can effectively manage nonlinear relationships among physicochemical attributes while simultaneously improving classification robustness and interpretability. The proposed methodology demonstrates how data visualization can reveal hidden quality patterns, identify influential variables, and support practical applications in quality assurance, winery management, and recommendation systems. The research contributes a structured conceptual framework that integrates predictive analytics and visualization into a unified decision-support architecture suitable for future intelligent wine quality management systems.
A Hybrid Predictive Learning Model for Red Wine Quality Assessment Using Classification and Visual Analytics
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Abstract
References
Aich, S., Sain, M., & Yoon, J. H. (2019). Prediction of different types of wine using nonlinear and probabilistic classifiers. In A. N. Krishna, K. C. Srikantaiah & C. Naveena (Eds.), Integrated intelligent computing, communication and security (pp. 11–19). Springer.
Chen, B., Rhodes, C., Crawford, A., & Hambuchen, L. (2014). Wineinformatics: Applying data mining on wine sensory reviews processed by the computational wine wheel. In 2014 IEEE International Conference on Data Mining Workshop, 142–149.
Hashmienejad, S. H. A., & Hasheminejad, S. M. H. (2017). Traffic accident severity prediction using a novel multi-objective genetic algorithm. International Journal of Crashworthiness, 22(4), 425–440.
Mahima, Gupta, U., Patidar, Y., Agarwal, A., & Singh, K. P. (2020). Wine quality analysis using machine learning algorithms. In Micro-Electronics and Telecommunication Engineering: Proceedings of 3rd ICMETE 2019, 11–18.
Palmer, J., & Chen, B. (2018). Wineinformatics: Regression on the grade and price of wines through their sensory attributes. Fermentation, 4(4), 84.
Reddy, Y. S., & Govindarajulu, P. (2017). An efficient user centric clustering approach for product recommendation based on majority voting: A case study on wine data set. International Journal of Computer Science and Network Security, 17(10), 103–111.
Thakkar, K., Shah, J., Prabhakar, R., Narayan, A., & Joshi, A. (2016). AHP and machine learning techniques for wine recommendation. International Journal of Computer Science and Information Technologies, 7(5), 2349–2352.
Ramamurthy, K., Gumber, S., Abdelfattah, W.M. et al. Human AI trust modeling in cognitive systems via ensemble learning and advanced feature engineering. Discov Artif Intell 6, 366 (2026). https://doi.org/10.1007/s44163-026-01255-7
Philip, P. G. (2026). Artificial Intelligence–Driven Intelligent Project Management: an integrated framework for planning, scheduling and control in engineering and construction projects. Journal of Engineering and Artificial Intelligence, 02(02), 01–09. https://doi.org/10.64142/jeai.2.2.50
Chowdhury, W. A. (2025). Blockchain for Sustainable Supply Chain Management: Reducing Waste Through Transparent Resource Tracking. Journal of Procurement and Supply Chain Management, 4(2), 28–34. https://doi.org/10.58425/jpscm.v4i2.435
S. R. Lankala, M. R. Marri, A. Jain, G. G. Battu, U. Lakhina and S. Singla, "Optimal Financial Fraud Detection and Alerting Mechanism in Cloud Computing Using Deep Belief Network," 2025 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), Windhoek, Namibia, 2025, pp. 743-748, doi: 10.1109/ETNCC66224.2025.11299665.