Detection of fortification of ginkgo products using nanoelectrospray ionization mass spectrometry
Bibliographic record
Abstract
We report here a negative ionization nanoelectrospray ionization mass spectrometry (nanoESI-MS) technique that simultaneously detects active components, terpenes and intact flavonol glycosides, and toxic ginkgolic acids in ginkgo products. Unlike the conventional methods that hydrolyze flavonol glycosides to flavonoids for analysis, this technique directly detects intact flavonol glycosides, enabling differentiation of these natural glycosides from the synthetic flavonoids. Thus, it allows the detection of fortification of ginkgo products, alleviating a common problem encountered by the conventional methods. Analysis of 14 commercial ginkgo products using this technique demonstrates large variations and deviation from the well-accepted standardized ginkgo extract. Four products showed evidence of fortification with synthetic surrogates. Two products were found to have toxic ginkgolic acids that exceed the 5 microg g(-1) limit by as much as 60000 fold. These results emphasize the importance of appropriate monitoring of ginkgo product quality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".