Variability of antioxidant properties, catechins, caffeine, L-theanine and other amino acids in different plant parts of Azorean Camellia sinensis
Bibliographic record
Abstract
During Camellia sinensis tea processing, manufacturers usually remove the internodes, which are classified as waste. This study presents the first determination of plant part contribution, particularly internodes, to green tea quality, in order to find the best blend to maximize impact on human health. Catechins, caffeine and free amino acid (FAA) profiles were determined by RP-HPLC/DAD, total phenolics (TPC) and total flavonoids (TFC) by Folin-Ciocalteu and colorimetric methodologies, respectively, and antioxidant activities by free radical-scavenging activity (FRSA), ferric-reducing antioxidant power (FRAP) and ferrous ion-chelating (FIC) methods. Individual esterified catechins content decreased as follows: epicatechin-3-gallate > epigallocatechin-3-gallate ≫ gallocatecin-3-gallate, and epicatechin derivatives content ranged from 63.91 to 91.22% of total catechins. Caffeine content was higher in internodes. L-theanine, histidine, asparagine, phenylalanine, glutamic acid and methionine were the major FAAs, and internodes contained the highest amounts of L-theanine and histidine (17 and 13.73 mg/g of sample, respectively). TPC ranged from 201.51 to 265.48 mg gallic acid equivalents/g dry extract (DE) and TFC ranged from 23.84 to 72.02 mg rutin equivalents/g DE. Internodes presented the lowest FRSA (EC 50 = 6.10–13.50 μg/mL), FRAP (EC 50 = 5.70–11.40 μg/mL) and FIC activity (36.96–79.21%). Bud presented the highest FRSA and FRAP, and bud+1st+2 nd leaves + internodes the highest FIC activity. The results revealed the potential contribution of the internodes to green tea quality and, consequently, to human health.
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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 | 0.001 | 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".