Characterization of ginseng saponins using electrospray mass spectrometry and collision-induced dissociation experiments of metal-attachment ions
Bibliographic record
Abstract
Electrospray mass spectrometry (ESMS) and collision-induced dissociation (CID) methodologies have been developed for the structural characterization of ginseng saponins (ginsenosides). Ginsenosides are terpene glycosides containing a triterpene core to which one to four sugars may be attached. They are neutral molecules which readily form molecular metal-attachment ions in positive ion ESMS experiments. In the presence of ammonium hydroxide intense deprotonated ions are generated. Both positive and negative ion ESMS experiments were found to be useful for molecular mass and structure determination of ten ginsenoside standards. Negative ion experiments made possible the determination of the molecular mass of each ginsenoside standard, the mass of the triterpene core and the masses and sequences of the sugar residues. Positive ion ESMS experiments with the alkali metal cations Li+ or Na+ and the transition metal cations Co2+, Ni2+ and Zn2+ were also useful in determining molecular masses. These alkali and transition metal cations form strongly bonded attachment ions with the ginsenosides. As a result, the CID mass spectra of the metal attachment ions show a variety of (structure characteristic) fragmentations. These experiments can be used to determine the identity of the triterpene core, the types and attachment points of sugars to the core and the nature of the O-glycosidic linkages in the appended disaccharides. Combining the results from the negative and positive ion experiments provides a promising approach to the structure analysis of this class of natural products.
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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 | 0.001 |
| 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 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".