Exploring DNA quantity and quality from raw materials to botanical extracts
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to explore the variability in DNA quality and quantity along a gradient of industrial processing of botanical ingredients from raw materials to extracts. METHODS: A data matrix was assembled for 1242 botanical ingredient samples along a gradient of industrial processing commonly used in the Natural Health Product (NHP) industry. Multivariate statistics was used to explore dependant variables for quality and quantity. The success of attaining a positive DNA test result along a gradient of industrial processing was compared among four biotechnologies: DNA barcoding, NGS, Sanger sequencing and qPCR. RESULTS: There was considerable variance in DNA quality and quantity among the samples, which could be interpreted along a gradient from raw materials with greater quantities (50-120 ng/μL) of DNA and longer DNA (400-500bp) sequences to extracts, which were characterized by lower quantities (0.1-10.0 ng/μL) and short fragments (50-150bp). CONCLUSIONS: Targeted molecular diagnostic tests for species identity can be used in the NHP industry for raw and processed samples. Non-targeted tests or the use of NGS for any identity test needs considerable research and development and must be validated before it can be used in commercial operations as these methods are subject to considerable risk of false negative and positive results. Proper use of these tools can be used to ensure ingredient authenticity, and to avert adulteration, and contamination with plants that are a health concern. Lastly these tools can be used to prevent the exploitation of rare herbal species and the harvesting of native biodiversity for commercial purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".