HPLC-DAD Fingerprints Combined With Multivariate Analysis of Epimedii Folium From Major Producing Areas in Eastern Asia: Effect of Geographical Origin and Species
Bibliographic record
Abstract
The growth location and plant variety may influence the active components and biological activities of plants used in phytomedicine. In this study, nine sets of different Epimedii Folium, from different representative cultivation locations and Epimedium species, were collected for comparison, using HPLC-DAD combined with multivariate analysis. The objective was to investigate the influence of geographical origin and Epimedium species on the quality of Epimedii Folium, and provide applicable guidance for cultivation and quality control of Epimedii Folium. Several Epimedium spp. sets were used to establish the HPLC-DAD fingerprints and 91 peaks (compounds) were selected for the multivariate analysis. Major compounds were analyzed by HPLC-DAD combined with principal component analysis (PCA). HPLC quantitative analysis of known bioactive compounds was performed. Application of PCA to HPLC data showed that Epimedium samples sharing the same geographical origin or species clustered together, indicating that both species and geographical origin have impacts on the quality of Epimedii Folium. The major bioactive flavonoid compounds, epimedin C, icariin and baohuoside I, were identified and quantified. The concentration of bioactive compounds was significantly influenced both by species and geographical origin. E. sagittatum from Sichuan showed the highest content of bioactive compounds. The results showed that both Epimedium species and geographical origin have strong impact into quality of Epimedii Folium. HPLC data combined with multivariate analysis is a suitable approach to inform the selection of cultivation areas and choose Epimedium spp. most suitable for different geographical areas, resulting in improved quality of Epimedii Folium.
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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) | 0.000 | 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 | 0.000 |
| 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".