Determination and Quantification of the Vinblastine Content in Purple, Red, and White Catharanthus Roseus Leaves Using RP-HPLC Method
Bibliographic record
Abstract
Purpose: To determine and quantify vinblastine in different varieties of Catharanthus roseus using reversed-phase HPLC method. Methods: The liquid chromatographic separation was performed using a reversed phase C18, Microsorb - MV column (250 mm x 4.6 mm, 5 µm) at room temperature and eluted with a mobile phase containing methanol – phosphate buffer (5 mM, pH 6.0) – acetonitrile with different proportion gradient elution at a flow rate of 2.0 mL min-1 and detection at 254 nm. Results: The HPLC method was utilized for the quantification of vinblastine in purple, red and white varieties of Catharanthus roseus leaves. The separation was achieved in less than 8 min. The peak confirmation was done based on the retention times and UV spectra of the reference substance. The method was validated with respect to linearity, precision, recovery, limit of detection and quantification. Results showed that the purple variety gives 1.2 and 1.5 times more vinblastine concentration compared to the white and pink varieties, respectively. Conclusion: The obtained results from different varieties are thus useful for the purpose of vinblastine production from Catharanthus roseus plant.
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.
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".