Comparative Analysis of the Polyphenols, Caffeine, and Antioxidant Activities of Green Tea, White Tea, and Flowers from Azorean Camellia sinensis Varieties Affected by Different Harvested and Processing Conditions
Bibliographic record
Abstract
This study evaluates the polyphenol profiles as well as caffeine (dry weight basis), and antioxidant activities of green tea (GTs), white tea (WTs), and flowers (Fl) samples from Azorean Camellia sinensis varieties affected by different harvested and processing conditions. Epicatechins derivatives, determined by RP-HPLC/PDAD, presented higher values in GTs with respect to WTs, decreasing as follows: epigallocatechin-3-gallate > epicatechin-3-gallate ≫ epicatechin ≫ epigallocatechin, and higher values in summer and early autumn than in spring. This was also accompanied by an in consistent withering time pattern. Esterified catechins were higher in all samples (100.8–312.3 mg/g) with respect to non-esterified catechins (15.1–37.7 mg/g). Caffeine (6.2–27.7 mg/g) decreased as follows: WTs > GTs ≫ Fl, and inconsistent seasonal and withering patterns were observed among the WTs. Total phenolics (125.9–295.4 mg gallic acid equivalents/g dried extract) and total flavonoids (35.2–69.7 mg rutin equivalents/g dried extract), determined by Folin–Ciocalteu and colorimetric methodologies, were higher in GTs than in WTs and Fl. Concerning the antioxidant patterns, the free radical scavenging activity (FRSA) and ferric reducing antioxidant power (FRAP) presented EC50 values ranges from 3.6 to 17.3 µg/mL and 4.8 to 16.5 µg/mL, respectively, and ferrous ion-chelating (FIC) activity ranged from 47.1 to 82.8%, highlighting that FRSA was better than butylated hydroxytoluene (BHT). Tea leaves exhibited, in general, higher activities with respect to tea Fl, and the WT sample plucked in summer and withered for 23 h showed the highest FRAP and FIC activity. In conclusion, this study shows the characteristic variation of GTs, WTs, and Fl of two tea varieties and may support crop quality improvement and promote the valorization of tea Fl.
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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 | 0.001 |
| 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".