Medicinal use of Sceletium: Characterization of Phytochemical Components of Sceletium Plant Species using HPLC with UV and Electrospray Ionization – Tandem Mass Spectroscopy
Bibliographic record
Abstract
PURPOSE: Sceletium plants have been used for its medicinal properties for centuries. However, there is a wide range of Sceletium plant species in which various alkaloidal components such as ∆7mesembrenone, mesembrenol, mesembranol, mesembrenone, mesembrine hydrochloride, epimesembranol and, sceletium A4 differ between species. Hence, to ensure the quality of Sceletium products used as a medicine, it is imperative to identify the appropriate species using both botanical and chemical methods. The chemical approach to identify and characterize the phytochemical composition of a particular species facilitates the choice of species that will provide the purported therapeutic outcome. Hence, specific analytical methods to identify relevant constituents from complex matrices are necessary. Although HPLC-UV detection is commonly used to identify and estimate phytochemical content of medicinal plants, use of mass spectroscopy (MS) and tandem mass spectroscopy (MS/MS) can unequivocally confirm their presence/absence based on characteristic ions and fragmentation patterns. METHODS: The various alkaloidal components were characterized by electrospray ionization (ESI) MS and MS/MS using an ionizing medium of 0.1% ammonium hydroxide in water mixed with acetonitrile. Compounds were purified and characterized for use as reference standards to identify the relevant alkaloidal constituents of several Sceletium plant species using HPLC with on-line UV-MS detection. RESULTS: ESI-MS provided the [M+H](+) ions with respective m/z values that related to the respective molecular weights 287, 289, 291, 287, 289, 324 and 291 for the above mentioned alkaloids, whereas, ESI MS/MS provided the characteristic fragment ions to confirm the structural identity of the individual alkaloids and subsequently used to confirm the presence and/or absence of specific alkaloids in various Sceletium plant samples. CONCLUSIONS: Whilst HPLC-UV detection has been a widely-used conventional analytical technique for both qualitative and quantitative analyses, the results highlight the necessity of ESI-MS detection to avoid erroneous identification of phytochemical components, particularly with mesembrine-type compounds which have closely related chemical structures. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".