A novel MS<sup>3</sup> experiment for quantifying ions with a linear ion trap
Bibliographic record
Abstract
Liquid chromatography coupled with tandem mass spectrometry has long been employed for the quantitation of molecules. With judicious selection of precursor and fragment ions, multiple-reaction monitoring assays can be developed rapidly for these experiments. However, there are cases where analyses struggle due to high background signals caused by matrix effects that interfere with the analytical signal. An alternative to MRMs involves using two stages of tandem mass spectrometry — an MS3 experiment. Although this technique can provide greater selectivity than MS/MS experiments, cycle times for MS3 experiments are typically longer than MRM-type experiments. Here, we present a quantitation technique employing an MS3 method with shorter cycle times than traditional linear ion trap MS3 scans. Termed “scan-free” MS3, this technique performs “mass analysis” by isolating the ions of interest in the linear ion trap and then emptying the trap of these ions. The signal will be due only to those preselected ions, resulting in an MS3 experiment with up to a ∼35% reduction in cycle times relative to standard MS3 experiments without loss of sensitivity. We compare the analytical performance of this method with MRMs, as well as standard MS3 experiments, finding equivalent or better performance from the scan-free MS3 method.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".