Analysis of a Tryptic Digest of Pig Hemoglobin Using ESI-FAIMS-MS
Bibliographic record
Abstract
The continuous gas-phase ion separation and atmospheric pressure focusing properties of high-field asymmetric waveform ion mobility spectrometry (FAIMS) offer significant advantages for the mass spectrometric analysis of tryptic digests of proteins. In this study, tryptic peptides of pig hemoglobin were examined by ESI-FAIMS-MS using a newly designed FAIMS device. The new, hemispherical geometry of the inner electrode served to deliver the ions, via the gas flows, to the center axis of the FAIMS analyzer, improving the sensitivity relative to previous prototypes. Mass spectra collected using this new FAIMS showed significantly less chemical background noise than conventional ESI-MS, while maintaining approximately the same absolute sensitivity as that observed with ESI-MS. As a consequence of the ion separation in FAIMS, the identification of the tryptic fragments was simplified and some peptides, such as the triply protonated WAGVANALAHK3+, that were obscured by the intense background of ESI-MS, were readily detected using ESI-FAIMS-MS. In addition, the FAIMS device was shown to separate isobaric ions at m/z 532.4. Correlations between CV and mass-to-charge ratio, as well as CV and ionic collision cross section, were evaluated for 38 peptide ions identified in the tryptic digest. The correlation between the CV of the peptide and the mass-to-charge ratio is very poor, indicating good orthogonality between the separation by FAIMS and the separation by mass spectrometry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".