Discrimination for geographical origin of <i>Panax quinquefolius</i> L. using <scp>UPLC Q‐Orbitrap MS</scp>‐based metabolomics approach
Bibliographic record
Abstract
L., is an important medicinal plant with multiple pharmacological effects and high nutritional value. American ginseng from different geographical origins varies in quality and price. However, there was no approach for discriminating American ginseng from different geographical origins to date. In this study, a metabolomic method based on the UPLC-Orbitrap fusion platform was established to comprehensively determine and analyze metabolites of American ginseng from America and Canada, Heilongjiang, Jilin, Liaoning, and Shandong provinces in China. A total of 382 metabolites were detected, including 230 saponins, 30 amino acids and derivatives, 27 organic acids and derivatives, 25 lipids, 17 carbohydrates and derivatives, 10 phenols, 8 nucleotides, and derivatives, as well as 35 other metabolites. Metabolite differences between North America and Asia producing areas were more obvious than within Asia. Twenty metabolites, contributed most to the differentiation of producing areas, were identified as potential markers with prediction accuracy higher than 91%. The results provide new insights into the metabolite composition of American ginseng from different origins, which will help discriminate origins and promote quality control of American ginseng.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".