Comparison of flow injection analysis electrospray mass spectrometry and tandem mass spectrometry and electrospray high‐field asymmetric waveform ion mobility mass spectrometry and tandem mass spectrometry for the determination of underivatized amino acids
Bibliographic record
Abstract
Twenty proteinogenic amino acids (AAs) were determined without derivatization using flow injection analysis followed by electrospray ionization mass spectrometry and tandem mass spectrometry (ESI-MS and ESI-MS/MS) and electrospray ionization high-field asymmetric waveform ion mobility mass spectrometry and tandem mass spectrometry (ESI-FAIMS-MS and ESI-FAIMS-MS/MS), in positive and negative ionization modes. Three separate sets of ESI-FAIMS conditions were used for the separation and detection of the 20 AAs. Typically ESI-FAIMS-MS showed somewhat improved sensitivity and significantly better signal-to-noise ratios than ESI-MS mainly due to the elimination of background noise. However, the difference between ESI-FAIMS-MS and ESI-MS/MS was significantly less. ESI-FAIMS was able to partially or completely resolve all the isobaric amino acid overlaps such as leucine, isoleucine and hydroxyproline or lysine and glutamine. Detection limits for the amino acids in ESI-FAIMS-MS mode ranged from 2 ng/mL for proline to 200 ng/mL for aspartic acid. Overall, ESI-FAIMS-MS is the preferred method for the quantitative analysis of AAs in a hydrolyzed yeast matrix.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".