Validated Identity Test Method for <i>Ginkgo biloba</i> NHPs Using DNA-Based Species-Specific Hydrolysis PCR Probe
Bibliographic record
Abstract
Background: There is considerable risk of adulteration of Ginkgo biloba herbal products in the natural health product (NHP) industry. Authentication of G. biloba products is challenging because of the standard, complex, analytical chemistry methods that may be too costly and not appropriate for both raw and finished products. Objective: We sought to develop and validate an alternative method to authenticate G. biloba herbal dietary supplements, based on the use of a species-specific hydrolysis PCR probe assay. Methods: A species-specific hydrolysis probe assay was developed, validated, and evaluated for the performance of the assay in accurately identifying the species of interest using the following analytical validation criteria: ( 1 ) specificity (accuracy) in identifying the target species ingredient, while not identifying other nontarget species, ( 2 ) sensitivity in detecting the smallest amount of the target material, and ( 3 ) reliability (repeatability and reproducibility) in detecting the target species in raw materials on a real-time PCR platform. Results: The species-specific hydrolysis probe assay was successfully developed for raw materials of G. biloba. The specificity of the assay was 100% to the target species. Efficiency of the assay was observed to be 99%, and the reliability of the assay was 100% for the raw/starting materials. Conclusions and Highlights: The method developed in this study is simple, rapid, and easy for supplement manufacturers to perform in their laboratories to ensure that their G. biloba supplements are authentic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".