Derivative Technology of DNA Barcoding (Nucleotide Signature and SNP Double Peak Methods) Detects Adulterants and Substitution in Chinese Patent Medicines
Bibliographic record
Abstract
Lonicerae japonicae Flos has been used to produce hundred kinds of Chinese patent medicines (CPMs) in China. Economically motivated adulterants have been documented, leading to market instability and a decline in consumer confidence. ITS2 has been used to identify raw medicinal materials, but it's not suitable for the identification of botanical extracts and complex CPMs. Therefore, a short barcode for the identification of processed CPMs would be profitable. A 34 bp nucleotide signature (5' CTAGCGGTGGTCGTACGATAGCCAATGCATGAGT 3') was developed derived from ITS2 region of Eucommiae Folium based on unique motifs. Mixtures of powdered Lonicerae japonicae Flos and Lonicerae Flos resulted in double peaks at the expected SNP (Single Nucleotide Polymorphisms) positions, of which the height of the peaks were roughly indicative of the species' ratio in the mixed powder. Subsequently we tested 20 extracts and 47 CPMs labelled as containing some species of Lonicera. The results revealed only 17% of the extracts and 22% of the CPMs were authentic, others exist substitution or adulterant; 7% were shown to contain both of two adulterants Eucommiae Folium and Lonicerae Flos. The methods developed in this study will widely broaden the application of DNA barcode in quality assurance of natural health products.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".