Machine learning-based prediction of sensory quality in tea blends using a semi-trained panel assessment
Abstract
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.
Fields of Study & Subjects
Tea Polyphenols and Effects
Sensory Analysis and Statistical Methods
Advanced Chemical Sensor Technologies
Publication details
- Journal / source
- Sustainable Food Technology
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 4
- Issue
- 3
- Pages
- 2805–2818
- Publication date
- 1 January 2026
- Language
- EN