Effects of processing method and age of leaves on phytochemical profiles and bioactivity of coffee leaves
Bibliographic record
Abstract
The use of coffee leaves as a novel beverage has recently received consumer interest, but there is little known about how processing methods affect the quality of final product. We applied tea (white, green, oolong and black tea) processing methods to process coffee leaves and then investigated their effects on phytochemical composition and related antioxidant and anti-inflammatory properties. Using Japanese-style green tea-processing of young leaves, and black tea-processing of mature (BTP-M) coffee leaves, produced contrasting effects on phenolic content, and associated antioxidant activity and nitric oxide (NO) inhibitory activity in IFN-γ and LPS induced Raw 264.7 cells. BTP-M coffee leaves also had significantly (P < .05) higher responses in NO, iNOS, COX-2, as well as a number of cytokines, in non-induced Raw 264.7. Our findings show that the age of coffee leaves and the type of processing method affect phytochemical profiles sufficiently to produce characteristic antioxidant and anti-inflammatory activities.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".