SEPARATION OF INDIVIDUAL CATECHINS FROM GREEN TEA USING SILICA GEL COLUMN CHROMATOGRAPHY AND HPLC
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".