Machine learning-based prediction of sensory quality in tea blends using a semi-trained panel assessment

This study presents a novel method of integrating an interpretable ML model and a semi-trained consumer-based panel for characterizing biochemical–sensory relationships. Adoption of this technique can reduce costs and dependency on expert intuition.