DNA Quality and Quantity Analysis of Camellia sinensis Through Processing from Raw Tea Leaves to a Green Tea Extract Product
Bibliographic record
Abstract
Although there has been some success using DNA barcoding to authenticate raw natural health product (NHP) ingredients, there are many gaps in our understanding of DNA degradation, which may explain low PCR and sequencing success in processed NHPs. In this study, we measured multiple DNA variables after each step in the processing of a green tea extract (GTE) product. We sampled plant material after each step of GTE processing: five at a Chinese tea farm (ten leaf samples per step) and five at a NHP processing facility (four subsamples from three batches of plant material per step). We hypothesized that processing treatments degrade and remove DNA from NHPs due to the physically damaging nature of the techniques, reflected by decreasing quantities of extractable genomic DNA, increasing proportion of small DNA fragments in genomic extracts, and decreasing QPCR efficiency (higher Ct values). We saw a 41% decrease in mean extractable genomic DNA through farm processing (p < 0.05) and a 99% decrease through facility processing (p < 0.05). There was a 26.3% decrease in mean DNA fragment size through farm processing and an 82% decrease through facility processing (p < 0.05). QPCR efficiency was reduced through processing, marked by significant increases in Ct values with 100bp and 200bp PCR targets (p < 0.05), and inability to amplify 300bp targets after all facility processing steps. While there was significant degradation and removal of DNA through processing, sufficiently intact DNA was able to be recovered from two of three batches of processed GTE, for the purpose of sequencing and 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".