DNA barcoding of species of <i>Bacopa</i> coupled with high-resolution melting analysis
Bibliographic record
Abstract
In Thailand, there are three species of Bacopa, namely, B. monnieri, B. caroliniana, and B. floribunda. Among these species of Bacopa, B. monnieri is the only medicinal species, used for the treatment of cognitive impairment and improvement of cognitive abilities because of its bioactive constituents, bacoside A and B. However, because of the similar characteristics of these species, it is difficult to differentiate among related species, resulting in confusion during identification. For this reason, and to ensure therapeutic quality for consumers, authentication is important. In this study, the three abovementioned species of Bacopa were evaluated using barcoding coupled with high-resolution melting (Bar-HRM) analysis based on primers designed for the trnL-F sequences of the three species. The melting profiles of the trnL-F amplicons of B. caroliniana and B. floribunda were clearly different from the melting profile of the trnL-F amplicon from B. monnieri; thus, the species could be discriminated by Bar-HRM analysis. Bar-HRM was then used to authenticate commercial products in various forms. The melting curves of the six commercial samples indicated that all the tested products contained genuine B. monnieri species. This method provides an efficient and reliable authentication system for future commercial herbal products and offers a reference system for quality control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".