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Visualizing chemical indicators: Spatial and temporal quality formation and distribution during black tea
Yujie Wang1, Zhengyu Ren1, Yuyu Chen1
1State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, China.
Food Chemistry
|September 17, 2022
Summary
Computer vision offers a low-cost, at-line method for monitoring black tea fermentation quality. This approach accurately predicts key quality indicators, supporting intelligent tea production.
Area of Science:
- Food Science
- Agricultural Engineering
- Computer Vision
Background:
- Fermentation is crucial for black tea quality.
- Current monitoring methods are expensive and lab-based.
Purpose of the Study:
- Evaluate at-line computer vision for real-time black tea fermentation monitoring.
- Correlate image color variables with key quality indicators.
Main Methods:
- Utilized a custom industrial camera for image acquisition.
- Extracted color variables from tea samples at various fermentation stages.
- Applied partial least-squares regression models.
Main Results:
- Demonstrated high prediction accuracy for catechins, theaflavins, and chlorophylls.
- Achieved residual prediction deviation values of 4.13, 3.53, and 3.39, respectively.
- Mapped spatial and temporal distributions of quality indicators.
Conclusions:
- Established a low-cost, at-line, real-time detection system for black tea fermentation.
- Provides technical support for intelligent and industrial black tea production.
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