Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?

Area of Science:

• Oenology and Viticulture
• Sensory Analysis
• Wine Chemistry

Background:

• Wine quality is crucial for producers and consumers, impacting marketing strategies.
• Predicting wine quality presents a significant challenge for wine science modeling.
• Understanding consumer perception of wine quality is key to effective marketing.

Purpose of the Study:

• To develop a mathematical model for predicting wine quality.
• To establish a conceptual and mathematical framework for wine quality prediction.
• To validate the predictive model using machine learning and expert data.

Main Methods:

• Dimensional analysis and the Buckingham Pi theorem were used to model relationships between chemical and physiochemical compounds.
• A mathematical model was developed using perceived wine quality indices from experts.
• Machine learning algorithms were applied to validate sensory and chemical concepts related to wine quality.

Main Results:

• A mathematical framework was developed to predict wine quality.
• The model demonstrated good agreement between predicted wine quality indices and expert ratings.
• Machine learning validation confirmed the relevance of sensory and chemical factors in predicting wine quality.

Conclusions:

• The developed mathematical model provides a reliable method for predicting wine quality.
• The study successfully integrated chemical analysis, expert judgment, and machine learning for wine quality assessment.
• This research offers valuable insights for wine producers aiming to enhance quality and marketing efforts.