Direct Identification and Quantification of Flavonoids and Their Structural Isomers Using Ambient Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
RATIONALE: Flavonoids are phenolic compounds with many health-benefiting properties. However, differentiating different types of flavonoids and their isomers is challenging due to their highly similar structures of various subtypes and different numbers and sites of substituents. Timely quality evaluation of flavonoid-based products is currently almost impossible. METHODS: An ambient ionization method of direct analysis in real time (DART) ion source and tandem mass spectrometry (MS) was used to characterize the fine structures of flavonoids. Different flavonoid subtypes and their isomers with varied numbers and sites of substituents were subjected to DART ionization and collision-induced fragmentation MS analysis. RESULTS: fragments through DART-tandem MS, enabling direct identification of various isomers within mixtures. An identification workflow was developed, culminating in the creation of a computational tool called FlavoFinder, which automatically determines flavonoid aglycone subtypes and their isomeric structures. CONCLUSIONS: The method and the structural elucidation program were successfully used for the qualitative and quantitative analysis of different flavonoid isomers from real samples. The analysis procedure is high-throughput and is capable of characterizing complex flavonoid structures without extensive sample pretreatment and front-end chromatographic separations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".