Machine learning for predicting aroma, flavour and preference of foods and beverages: case studies with small and imbalanced datasets

Key Points

• Findings reveal machine learning can effectively predict aroma and flavor preferences in foods and beverages.
• Crucial metrics highlight accuracy rates exceeding 85% across various classifiers used on imbalanced datasets.
• Analysis of small datasets underscores the importance of feature extraction methods in predicting sensory characteristics.
• Significance of findings may enable advancements in personalized food and beverage recommendations.