Trihydroxyflavones from<i>Scutellaria baicalensis</i>: Separation by a Facile MEKC Technique and Comparison to an Analytical HPLC Method
Bibliographic record
Abstract
Phenolic were extracted from the roots of Scutellaria baicalensis Georgi (Labiatae) using methanol. The phenolics of the crude extract were examined by high‐performance liquid chromatography (HPLC) using an analytical C18 column coupled with ultraviolet‐diode array detection (UV‐DAD). Chromatograms were compared with those acquired by micellar electrokinetic chromatography (MEKC) with UV‐DAD. A good separation of the phenolics from the crude extract was achieved by the electrophoretic technique, and in a shorter time than by HPLC. Two dominant flavones, believed to be 5,6,7‐trihydroxyflavone and 5,6,7‐trihydroxyflavone‐7‐O‐β‐D‐glucopyranosiduronate, which are commonly referred to as baicalein and baicalin, respectively, were then isolated from the crude extract using a semi‐preparative HPLC method on a RP‐18 column. The identities of the separated trihydroxyflavones were confirmed by NMR spectroscopies and mass spectrometry as being baicalein (1) and baicalin (2). The employment of MEKC coupled with UV‐DAD as a technique to separate and to identify phenolic compounds, or their classes in natural products research, is expected to expand over the next decade.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".