Research progress on the response of tea catechins to drought stress
Bibliographic record
Abstract
Drought stress (DS) is the most important abiotic stress affecting yield and quality of tea worldwide. DS causes oxidative stress to cells due to the accumulation of reactive oxygen species (ROS). As non-enzymatic antioxidants, tea catechins can scavenge excess ROS in response to DS. Further, catechin accumulation contributes to the formation of oxidative polymerization products (e.g. theaflavins and thearubigins) that improve the quality of black tea. However, there are no systematic reports on the response of tea catechins to DS. First, we reviewed the available literature on the response of tea plants to DS. Second, we summarized the current knowledge of ROS production in tea leaves under DS and typical antioxidant response mechanisms. Third, we conducted a detailed review of the changes in catechin levels in tea under different drought conditions. We found that the total amounts of catechin and o-quinone increased under DS conditions. We propose that the possible mechanisms underlying tea catechin accumulation under DS conditions include (i) autotrophic formation of o-quinone, (ii) polymerization of proanthocyanidins that directly scavenge excess ROS, and (iii) formation of metal ion complexes and by influencing the antioxidant systems that indirectly eliminate excess ROS. Finally, we discuss ways of potentially improving black tea quality using drought before picking in the summer/fall dry season. In summary, we mainly discuss the antioxidant mechanisms of tea catechins under DS and the possibility of using drought to improve black tea quality. Our review provides a theoretical basis for the production of high-quality black tea under DS conditions. © 2021 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".