Comprehensive analysis of tea concentrates with machine learning assisted sensory characterization

This study investigated physicochemical, biochemical, microbiological, and sensory changes in seven tea concentrate types during 30-day frozen storage (-18°C), and developed machine learning models to predict sensory quality from chemical parameters. Tea concentrates prepared from FBOP, FP, BOP, GBOP, CD, RD, and PF leaves underwent progressive transformations: total phenolic content declined 19–30%, theaflavins decreased 70–80%, while highly polymerized substances increased 7–15%. Sample 3 (Broken Orange Pekoe) achieved the highest sensory scores (38.76/45, "Excellent") through balanced polyphenol retention, while Sample 7 exhibited the poorest performance (35.31/45, "Moderate") due to severe degradation. All samples remained microbiologically safe (bacterial/fungal counts