Exploring Flavors Through AI: The Future of Culinary Taste Prediction

This study assesses the capacity of artificial intelligence (AI) algorithms to mimic a human's sense of taste, specifically in the context of wines. We utilize wine data and machine learning tests to compare the performance of ML models, including Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine, against that of a real sommelier. Our findings show that the Random Forest model outperforms all others in accuracy. Moreover, our results uncover new insights into wine tasting. While our human tongue can detect 11 variables from our dataset, only four of these variables are used by the brain to discern wine flavors. This discovery challenges the previously held belief in the complexity of our sensory system. Our methodology paves the way for future research by streamlining data collection and enhancing its cost efficiency and accuracy by focusing on these essential variables rather than the entire set of eleven.