Identification and Quantification of Anthocyanin and Catechin Compounds in Purple Tea Leaves and Flakes
Bibliographic record
Abstract
). This study investigated purple tea leaves (non-oxidized) and flakes (water extractable) to thoroughly identify their composition of anthocyanins and catechins and to study the effect of a water extraction process on their compositional properties in comparison with green tea. Anthocyanin and catechin compounds were separated and quantified using UPLC, and their identity was confirmed using LC-MS/MS in positive and negative ionization modes. Delphinidin was the principal anthocyaninidin in purple tea, while cyanidin came in second. The major anthocyanin pigments in purple tea were delphinidin-coumaroyl-hexoside followed by delphinidin-3-galactoside and cyanidin-coumaroyl-hexoside. The water extraction process resulted in substantial reductions in anthocyanins in purple tea flakes. There were no anthocyanin compounds detected in green tea samples. Both purple and green tea types were rich in catechins, with green tea containing higher concentrations than purple tea. The main catechin in purple or green tea was epigallocatechin gallate (EGCG) followed by either epicatechin gallate (ECG) or epigallocatechin (EGC), subject to tea type. The extraction process increased the concentration of catechins in both purple and green tea flakes. The results suggest that purple tea holds promise in making healthy brews, natural colorants and antioxidants and/or functional ingredients for beverages, cosmetics and healthcare industries due to its high content of anthocyanins and catechins.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".