Development of a Spot Test (ST) Based on the Nucleic Acid Amplification Test (NAAT) for Ginseng Species Authentication
Bibliographic record
Abstract
It is very challenging to distinguish between Panax ginseng (P. ginseng) and Panax quinquefolius (P. quinquefolius) based on their physical appearances. Therefore, a molecular test that can be performed in commercial settings is needed to address this challenge. We have selected a locus containing a single nucleotide polymorphism (SNP) site in the Dammarendiol-II Synthase (DS) gene to differentiate between P. ginseng and P. quinquefolius. An isothermal nucleic acid amplification test (NAAT) developed previously was adapted and used on a spot test (ST) format consisting of a sample pad, a nitrocellulose membrane, and an adsorbent pad. An ST with a colorimetric detection method has been developed with visual detection based on 20-nm gold nanoparticles. The presence of the target DNA will generate a red color signal, which is visible to the naked eye. The assay comprises the capture probes, constructed based on the DS gene of ginseng, and the ends of the probe sequences sit on the SNP site for the P. ginseng and P. quinquefolius sequences; the detection probe employed in the assay is tagged with gold. The NAAT amplifies the extracted plant genomic samples and enhances the detection of specific SNPs. This NAAT technique was used to authenticate two ginseng root samples, showing greater specificity ratios than our previous work.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".