Nontarget Analysis of Urine by Electrospray Ionization-High Field Asymmetric Waveform Ion Mobility-Tandem Mass Spectrometry
Bibliographic record
Abstract
Nearly a decade after first commercialization, high field asymmetric waveform ion mobility spectrometry (FAIMS) has yet to find its place in routine chemical analysis. Prototypes have been used to demonstrate the utility of this separation technique combined with mass spectrometry (MS). Unfortunately, first generation commercial FAIMS instruments have gone practically unused by early adopters. Here, we show this to be due to poor ion transmission in the FAIMS-MS source interface. We present simple instrumental modifications and optimization of experimental conditions to achieve good performance from the first generation commercial FAIMS device (the Ionalytics Selectra) coupled to a high resolution Q-TOF-MS. In combination with nanospray ionization, we demonstrate for the first time the nontarget analysis of urine by FAIMS with minimal sample preparation. We show the unique suitability of electrospray ionization (ESI)-FAIMS-MS for identification of low abundance species such as urinary biomarkers of damage of nucleic acids in a complex biological matrix. The elimination of electrospray noise and matrix components by FAIMS and the continuous flow of analytes through FAIMS for accurate and tandem mass analysis produce high quality spectral data suitable for structural identification of unknowns. These characteristics make ESI-FAIMS-MS ideal for nontarget identification, even when compared to high efficiency LC-ESI-MS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".