Analysis and Stability of the Constituents of Artichoke and St. John's Wort Tinctures by HPLC–DAD and HPLC–MS
Bibliographic record
Abstract
In continuing our investigations on tinctures, which represent both herbal drug preparations and herbal medicinal products, 40% and 60% v/v tinctures of artichoke and St. John's wort were investigated. Artichoke is largely used in hepatic disorders, while St. John's wort is an anti-inflammatory, antidepressant, and healing agent. Both herbal drugs contain various constituents, although the compounds responsible for the main effects have not yet been completely identified. However, caffeoylquinic acids and flavones seem to be of crucial importance for the activity of artichoke, as well as flavonoids, naphthodianthrones, and phloroglucinol derivatives for St. John's wort, and they are used as marker constituents. Thus, quantification of all these constituents was performed using high-performance liquid chromatography-diode array detection (HPLC-DAD) and HPLC--mass spectrometry (MS) analyses with rutin as external standard. In addition the stability of the constituents of these tinctures from accelerated and long-term testing was also evaluated. From the results it was evidenced that constituent content depends on the solvent used for the extraction. The stability was also shown to be very different and seems to be related to the water content of the tinctures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".