Seasonal Variations of Phenolic Compounds in Australia-Grown Tea (<i>Camellia sinensis</i>)
Bibliographic record
Abstract
Seasonal variations of phenolic compounds in fresh tea shoots grown in Australia were studied using an HPLC method. Three principal tea flavanols [epigallocatechin gallate (EGCG), epicatechin gallate (ECG), and epigallocatechin (EGC)] and four grouped phenolics [total catechins (Cs), total catechin gallates (CGs), total flavanols (Fla), and total polyphenols (PPs)] in fresh tea shoots were analyzed and compared during the commercial harvest seasons from April 2000 to May 2001. The levels of EGCG, ECG, and CGs in the fresh tea shoots were higher in the warm months of April 2000 (120.52, 34.50, and 163.75 mg/g, respectively) and May 2000 (128.63, 44.26, and 183.83 mg/g, respectively) and lower during the cool months of July 2000 (91.39, 35.16, and 132.30 mg/g, respectively), August 2000 (91.31, 31.56, and 128.64 mg/g, respectively), and September 2000 (96.12, 33.51, and 136.90 mg/g, respectively). Thereafter, the levels increased throughout the warmer months from October to December 2000 and remained high until May 2001. In the warmer months, the levels of EGCG, ECG, and CGs were in most cases significantly higher (P < 0.05) than those in the samples harvested in the cooler months. In contrast, the levels of EGC and Cs were high and consistent in the cooler months and low in the warmer months. The seasonal variations of the individual and grouped catechins were significant (P < 0.05) between the cooler and warmer months. This study revealed that EGCG and ECG could be used as quality descriptors for monitoring the seasonal variations of phenolics in Australia-grown tea leaves, and the ratio (EGCG + ECG)/EGC has been suggested as a quality index for measuring the differences in flavanol levels in fresh tea shoots across the growing seasons. Mechanisms that induce seasonal variations in tea shoots may include one or all three of the following environmental conditions: day length, sunlight, and/or temperature, which vary markedly across seasons. Therefore, further studies under controlled conditions such as in a greenhouse may be required to direct correlate flavonoid profiles of green tea leaves with their yields and also to with conditions such as rainfall and humidity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".