Comprehensive analysis of Ginkgo tablets by easy ambient sonic spray ionization mass spectrometry
Bibliographic record
Abstract
We report here that easy ambient sonic spray ionization − mass spectrometry (EASI−MS) allowed direct, rapid, and comprehensive analysis of active components (e.g., terpenetrilactones and intact flavonol glycosides) in Ginkgo tablets. Unlike conventional methods that hydrolyze flavonol glycosides to flavonoids prior to analysis, EASI−MS detects intact flavonol glycosides directly from the tablets enabling differentiation of these natural glycosides from the synthetic flavonoids. Adulteration of Ginkgo tablets is therefore readily recognized in less than 10 s measurement time. Sample preparation is simple and requires only 0.5 mL of methanol and a mortar and pestle. Additionally, no reference standards are needed and a comparison is made to a certified Ginkgo tablet. To demonstrate these abilities, 22 commercial Ginkgo tablets were analyzed by EASI−MS. The data revealed large variations of ingredients and strong deviations from the specified Ginkgo extract levels. These results emphasize the importance of monitoring Ginkgo products using appropriate methodologies as suggested in our work. The technique may replace more demanding LC−MS/MS methods, which can achieve comparable results but are considerably more labor-intensive. Importantly, the presented method is cheaper, faster, and much simpler to operate than chromatography-based mass spectrometric techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".