DNA Barcoding for the Identification of Botanicals in Herbal Medicine and Dietary Supplements: Strengths and Limitations
Bibliographic record
Abstract
In the past decades, the use of traditional medicine has increased globally, leading to a booming herbal medicine and dietary supplement industry. The increased popularity of herbal products has led to a rise in demand for botanical raw materials. Accurate identification of medicinal herbs is a legal requirement in most countries and prerequisite for delivering a quality product that meets consumer expectations. Traditional identification methods include botanical taxonomy, macroscopic and microscopic examination, and chemical methods. Advances in the identification of biological species using DNA-based techniques have led to the development of a DNA marker-based platform for authentication of plant materials. DNA barcoding, in particular, has been proposed as a means to identify herbal ingredients and to detect adulteration. However, general barcoding techniques using universal primers have been shown to provide mixed results with regard to data accuracy. Further technological advances such as mini-barcodes, digital polymerase chain reaction, and next generation sequencing provide additional tools for the authentication of herbs, and may be successful in identifying processed ingredients used in finished herbal products. This review gives an overview on the strengths and limitations of DNA barcoding techniques for botanical ingredient identification. Based on the available information, we do not recommend the use of universal primers for DNA barcoding of processed plant material as a sole means of species identification, but suggest an approach combining DNA-based methods using genus- or species-specific primers, chemical analysis, and microscopic and macroscopic methods for the successful authentication of botanical ingredients used in the herbal dietary supplement industry.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".