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SEPARATION OF INDIVIDUAL CATECHINS FROM GREEN TEA USING SILICA GEL COLUMN CHROMATOGRAPHY AND HPLC

2003· article· en· W2040249451 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.

Bibliographic record

VenueJournal of Food Lipids · 2003
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsChemistryChromatographyCatechinHigh-performance liquid chromatographyFraction (chemistry)Silica gelAcetic acidEpicatechin gallateEpigallocatechin gallateMethanolColumn chromatographySolventPolyphenolOrganic chemistryAntioxidant

Abstract

fetched live from OpenAlex

ABSTRACT Crude catechin mixtures from green tea were separated into six fractions using a silica gel column chromatography and a chloroform‐methanol‐water (65:35:10, v/v/v, lower phase) solvent system. Fraction I was free of catechins, fraction II contained epicatechin (EC), fraction III had epicatechin and epigallocatechin (EGC), fraction IV possessed EGC, fraction V contained EGC, epicatechin gallate (ECG) and epigallocatechin gallate (EGCG), and fraction VI had EGCG. EC and EGC were separated from fractions II, III and IV using HPLC with a RP‐18 semipreparative column and a water‐dimethylformamide‐methanol‐acetic acid (157:40:2:1, v/v/v/v) solvent system. For isolation of EGC, ECG and EGCG from fractions V and VI a water‐acetonitrile‐methanol‐acetic acid (159:36:4:1, v/v/v/v) solvent system was employed. Chemical structures of purified catechins were further confirmed by ESI‐MS.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.374

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 head0.024
GPT teacher head0.285
Teacher spread0.261 · 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