Quantification of Actaea racemosa L. (black cohosh) from some of its potential adulterants using qPCR and dPCR methods
Bibliographic record
Abstract
The demand for popular natural health products (NHPs) such as Black Cohosh is increasing considerably, which in turn challenges quality assurance (QA) throughout the supply chain. To detect and quantify the target species present in a given NHP, DNA-based molecular techniques such as Real-time quantitative PCR (qPCR) and digital PCR (dPCR) are standard tools in the food and pathogen testing industries. There is a gap in the literature concerning validated quantitative PCR methods for botanicals that can be utilized for QA and good manufacturing practices. The objective of this study is to develop an efficient quantification method using qPCR and dPCR techniques for the detection and quantification of Actaea racemosa (Black cohosh) NHPs from its potential adulterants. These developed methods are validated for applicability on commercial NHPs. Species-specific hydrolysis probe assays were designed to analyze the black cohosh NHPs using qPCR and dPCR techniques. The results confirmed that the developed qPCR and dPCR methods are highly precise for identifying and quantifying black cohosh NHPs, indicating their potential applicability in future routine industrial and laboratory testing. This enables a single qPCR test to determine not only the presence of a specific botanical, but also the amount when mixed with an adulterant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".