Rapid analysis of <i>α</i> ‐dicarbonyl compounds by laser desorption/ionization mass spectrometry using 9‐(3,4‐diaminophenyl)acridine (DAA) as a reactive matrix
Bibliographic record
Abstract
A rapid, sensitive and selective method has been developed for the analysis of alpha-dicarbonyls using a readily ionizable compound, 9-(3,4-diaminophenyl)acridine (DAA), as a reactive matrix (derivatizing agent and ionization efficiency enhancer), by reactive matrix laser desorption/ionization time-of-flight mass spectrometry (RM-LDI-TOF MS). The reaction between the DAA and alpha-dicarbonyls resulted exclusively in formation of vacuum-stable dicarbonyl-quinoxaline acridine derivatives that were found to possess excellent ionization efficiency in positive ion mode, without the need to use an additional matrix. The alpha-dicarbonyls used as test compounds included methylglyoxal, dimethylglyoxal, and diphenylglyoxal. Both one-pot and rapid on-plate chemical modification approaches were employed with no extraction or purification necessary. The approach is particularly suitable for high-throughput analysis. The method was found to be selective and specific, with alpha-dicarbonyls unequivocally identified, even in complex matrices, e.g. beer. The figures of merit: relative standard deviation (RSD) 6.9-17%, (n = 4); limit of detection (LOD) < or =0.3 ng mL(-1) for the three standards tested using the one-pot derivatization method; and a good linear calibration curve using an internal standard derivatized in situ (R(2) > or = 0.979), demonstrate the applicability of the technique and its utility in improving the sensitivity and precision of the LDI analysis of small molecules.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".