Research on Concentration of Ginsenoside in Different Medical Parts of Panax quinquefolium by HPLC-MS~n
Bibliographic record
Abstract
Objective: High-performance liquid chromatography array mass spectrometry(HPLC-MS) method was developed for determination of the concentration of 10 different ginsenosides of different medical parts of Panax quinquefolium,which collected from original habitat in Ontario of Canada.Method: Sample solution was separated on a DIKMA diamonsil(4.6 mm × 250 mm,5 μm).Acetonitrile-0.05% phosphoric acid aqueous solution and acetonitrile-0.05% acetic acid aqueous solution were used as mobile phase and the flow rate through the HPLC column was 0.3 mL.min-1 and the entire effluent was directed to the mass spectrometer.The column temperature was kept at 35 ℃ and the UV detection wavelength was set at 203 nm.MS analysis was monitored in positive mode.The conditions of ESI source were as follows: sheath gas flow rate,10 L.min-1;sweep gas flow rate,10 L.min-1;spray voltage,4.5 kV;capillary temperature,320 ℃;capillary voltage 30 V.Result: All calibration curves showed good linearity(r 0.999) within the test ranges.The average recovery of the method was between 95% and 102%,RSD 2.68%.The concentration of single ginsenoside of root,stem and leaf was quite different;that of Re and Rb1 of root and that of Rb2 and Rb3 of leaf was higher than others;that of stem was lower than that of root and leaf,moreover that of some samples of leaf was higher than that of root.Conclusion: The method is simple,accurate,replicate and suitable for the determination of P.quinquefolium.It is suggested that the leaf of P.quinquefolium can be used a new resource of ginsenoside.
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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".