HPLC Quantification of Hydroxycinnamic and Organic Acids of Canadian Goldenrod (Solidago canadensis L.)
Bibliographic record
Abstract
Background: Canadian goldenrod (Solidago canadensis L.) is a medicinal plant widely used in traditional medicine across the world for several hundred years. According to literature data, S. canadensis contains various groups of biologically active substances, including tannins, flavonoids, etc. The aim of the study was to identify and quantify hydroxycinnamic and organic acids in aerial parts of Canadian goldenrod, as these groups of substances demonstrate a broad spectrum of therapeutic activities. Materials and Methods: Ethanolic extracts of S. canadensis, gathered in Central Russia, were analyzed using Highperformance liquid chromatography (HPLC). Hydroxycinnamic acids (HCA) determination was carried out by HPLC method with UV detection at 330 nm using HCA Reference standards (RS). Organic acids (OA) determination was performed in the same manner, utilizing UV detection at 210 nm and corresponding OA RS. Results: It was established that S. canadensis HCA composition is represented by cichoric, caffeic, chlorogenic, quinic and ferulic acids. The total HCA content in was 1.16 g 10.7 mg / 100 g. Main OA, found in S. canadensis, are ascorbic, citric, tartaric, succinic, gallic, malic, oxalic and fumaric acids, with the total OA content of 426.5 mg 6.4 mg / 100 g. Conclusion: The described HPLC method was successfully used for analysis of S. canadensis aerial parts ethanolic extracts. The method can be utilized for HCA and OA identification and quantification in both herbal raw material and herbal medicinal products containing Canadian goldenrod.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".