Use of digitoxin and digoxin as internal standards in HPLC analysis of triterpene saponin‐containing extracts
Bibliographic record
Abstract
INTRODUCTION: Saponins are widely distributed complex plant glycosides possessing a variety of structure-dependent bioactivities. Quantitation of individual saponins is difficult due to lack of available standards, mainly as a consequence of purification difficulties. Determination of total saponin content can be problematic, often relying on non-specific methods based on butanol solubility, haemolytic activity or formation of coloured derivatives. OBJECTIVE: To develop a general quantitative method based on the use of the readily available cardenolides, digitoxin (1) and digoxin (2), as internal standards in an HPLC-PAD-based analysis. METHODOLOGY: The cardenolides were run at a variety of concentrations to establish linearity and reproducibility of detector response and then evaluated as internal standards for quantitation of triterpene saponins in several plant-derived extracts by HPLC-PAD. Mixtures of saponins, largely freed from other extractables, were obtained by fractionation of total extracts on solid phase extraction columns (SPE) employing a water-methanol gradient and used for construction of calibration curves. Saponin identification and structural information was obtained via a single quadrupole mass detector using electrospray ionisation in negative ion mode (ESI(-)). RESULTS: Saponin contents in six samples from five species were determined and compared with literature results and a gravimetric method based on butanol-water partitioning. Results were generally consistent with literature reports and superior to gravimetric butanol-water partitioning. CONCLUSION: Digitoxin and digoxin are useful as internal standards in HPLC estimation of saponin content. Saponins from different species having similar structures and molecular weights afford similar calibration curves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".