Analysis of flavonoids: Tandem mass spectrometry, computational methods, and NMR
Bibliographic record
Abstract
Due to the increasing understanding of the health benefits and chemopreventive properties of flavonoids, there continues to be significant effort dedicated to improved analytical methods for characterizing the structures of flavonoids and monitoring their levels in fruits and vegetables, as well as developing new approaches for mapping the interactions of flavonoids with biological molecules. Tandem mass spectrometry (MS/MS), particularly in conjunction with liquid chromatography (LC), is the dominant technique that has been pursued for elucidation of flavonoids. Metal complexation strategies have proven to be especially promising for enhancing the ionization of flavonoids and yielding key diagnostic product ions for differentiation of isomers. Of particular value is the addition of a chromophoric ligand to allow the application of infrared (IR) multiphoton dissociation as an alternative to collision-induced dissociation (CID) for the differentiation of isomers. CID, including energy-resolved methods, and nuclear magnetic resonance (NMR) have also been utilized widely for structural characterization of numerous classes of flavonoids and development of structure/activity relationships.The gas-phase ion chemistry of flavonoids is an active area of research particularly when combined with accurate mass measurement for distinguishing between isobaric ions. Applications of a variety of ab initio and chemical computation methods to the study of flavonoids have been reported, and the results of computations of ion and molecular structures have been shown together with computations of atomic charges and ion fragmentation. Unambiguous ion structures are obtained rarely using MS alone. Thus, it is necessary to combine MS with spectroscopic techniques such as ultraviolet (UV) and NMR to achieve this objective. The application of NMR data to the mass spectrometric examination of flavonoids is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".