Determination of Hydrastine and Berberine in Goldenseal Raw Materials, Extracts, and Dietary Supplements by High-Performance Liquid Chromatography with UV: Collaborative Study
Bibliographic record
Abstract
A multilaboratory collaborative study was conducted on a high-performance liquid chromatographic (HPLC) method utilizing UV detection, previously validated using AOAC single-laboratory validation guidelines for determination of hydrastine and berberine in goldenseal (Hydrastis canadensis L.) raw materials, extracts, and dietary supplements at levels ranging from 0.4 to 6% (w/w). Nine collaborating laboratories determined the hydrastine and berberine content in 8 blind samples. Sample materials included powdered botanical raw materials, whole root material, and 4 finished product dietary supplements containing either goldenseal powdered root material or extract. The materials were extracted with an acidified water and acetonitrile solution. HPLC analyses of the extracts were performed on a C18 column using UV detection at 230 nm. Results for powdered root material and capsule products ranged from about 0.2% (w/w) for each alkaloid to about 4% (w/w) for each alkaloid. Liquid tincture results were approximately 4000-5000 microg/mL for each alkaloid. Reproducibility relative standard deviations (RSDR) for hydrastine ranged from 2.68 to 6.65%, with HorRat values ranging from 0.77 to 1.89. RSDR for berberine ranged from 5.66 to 7.68%, with HorRat values ranging from 1.32 to 2.12. All finished products containing goldenseal extract yielded HorRat values <2.0. Based on these results, the method is recommended for Official First Action for determination of hydrastine and berberine in goldenseal raw materials and dietary supplement finished products containing powdered goldenseal and goldenseal extract.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".