Genome skimming and NMR chemical fingerprinting provide quality assurance biotechnology to validate Sarsaparilla identity and purity
Bibliographic record
Abstract
Sarsaparilla is a popular natural health product (NHP) that has been reported to be one of the most adulterated botanicals in the marketplace. Several plausible explanations are documented including economically motivated product substitution, unintentional errors due to ambiguous trade name associated with several different taxa, and wild harvesting of incorrect non-commercial plants. Unfortunately, this includes the case of an adulterant species Decalepis hamiltonii, a Red listed medicinal plant species by the International Union for Conservation of Nature (IUCN) and declared as a species with high conservation concern by the National Biodiversity Authority of India (NBA). This study provides validated genomic (genome skimming & DNA probes) and metabolomic (NMR chemical fingerprints) biotechnology solutions to prevent adulteration on both raw materials and finished products. This is also the first use of Oxford Nanopore on herbal products enabling the use of genome skimming as a tool for quality assurance within the supply chain of botanical ingredients. The validation of both genomics and metabolomics approach provided quality assurance perspective for both product identity and purity. This research enables manufactures and retailers to verify their supply chain is authentic and that consumers can enjoy safe, healthy 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".