DNA Quality and Quantity Analysis of <i>Camellia sinensis</i> Through Processing from Fresh Leaves to a Green Tea Extract
Bibliographic record
Abstract
Background: Although there has been some success using DNA barcoding to authenticate raw natural health product (NHP) botanical ingredients, there are many gaps in our understanding of DNA degradation, which may explain low PCR and sequencing success in processed NHPs. Objective: In this study, we measured multiple DNA variables after each step in the processing of a green tea extract in order to document DNA quality and quantity. Methods: We sampled plant material after each step of green tea extract processing: five steps at a Chinese tea farm ( n = 10) and five at an NHP processing facility ( n = 3). We hypothesized that processing treatments degrade and remove DNA from NHPs, reflected by decreasing quantities of extractable genomic DNA (gDNA), an increasing proportion of small DNA fragments in genomic extracts, and decreasing quantitative PCR (QPCR) efficiency [higher cycle threshold (C t ) values]. DNA from end-production green tea extract was sequenced in order to try to validate material as the botanical of interest. Results: We saw a 41.1% decrease in mean extractable gDNA through farm processing ( P < 0.01) and a 99.7% decrease through facility processing ( P < 0.05). There was a 26.3% decrease in mean DNA fragment size through farm processing ( P < 0.001) and an 82.0% decrease through facility processing ( P < 0.05). QPCR efficiency was reduced through processing, marked by significant increases in C t values with 100 base pair (bp) and 200 bp PCR targets ( P < 0.05), and an inability to amplify 300 bp targets when using DNA template from end-production green tea extract. Conclusions: Although there was significant degradation and removal of DNA through processing, sufficiently intact DNA was able to be recovered from highly processed green tea extract for further sequencing and identification. Highlights: This work addresses a key gap in the understanding of DNA degradation through processing and provides useful information to consider when designing molecular diagnostic techniques for NHP identification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".