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Effects of processing method and age of leaves on phytochemical profiles and bioactivity of coffee leaves

2017· article· en· W2776680734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFood Chemistry · 2017
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsPhytochemicalFood scienceAntioxidantChemistryCatechinTraditional medicineBlack teaPolyphenolBiochemistryMedicine

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.298
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it