RP‐HPLC determination of phenylalkanoids and monoterpenoids in <i>Rhodiola rosea</i> and identification by LC‐ESI‐TOF
Bibliographic record
Abstract
An HPLC method permitting the simultaneous determination of fourteen analytes (phenylalkanoids and monoterpenoids) from the roots of Rhodiola rosea was developed. A separation was achieved within 35 min using C(18) column material and a water-acetonitrile mobile phase, both containing a 0.05% phosphoric acid gradient system and a temperature of 53 degrees C. The method was validated for linearity, repeatability, limits of detection and limits of quantification. The limits of detection and limits of quantification of 14 phenylalkanoids and monoterpenoids were found to be 0.20-1.0 and 0.5-3.5 microg/mL, respectively. The wavelengths used for quantification of phenylalkanoids and monoterpenoids with a diode array detector were 205, 220 and 251 nm. The method was used to analyze the roots of two species of Rhodiola and commercial extracts of R. rosea and provides preliminary evidence of phytochemical differences between North American and Eurasian populations of R. rosea. LC-mass spectrometry coupled with electrospray ionization (ESI) interface method is described for the identification of phenylalkanoids and monoterpenoids in various Rhodiola samples. This method involved the use of the [M + H](+), [M + NH(4)](+) and [M + Na](+) ions in the positive ion mode with extractive ion monitoring (EIM).
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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) | 0.001 | 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 | 0.001 |
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".