Chiral Derivatization Enables High-Resolution Ion Mobility Spectrometry of 30 Amino Acid Enantiomers
Bibliographic record
Abstract
Chiral analysis of amino acid enantiomers is essential due to their frequently divergent biological activities. However, ion mobility spectrometry–mass spectrometry (IMS–MS) cannot directly resolve underivatized amino acid enantiomers due to their identical physicochemical properties. This study developed a derivatization-based analytical method utilizing (S)-N-(4-nitrophenoxycarbonyl) phenylalanine methoxyethyl ester ((S)-NIFE) as a chiral derivatization agent to achieve the stereoselective modification of amino acids. The resulting diastereomers form metal ion adducts, which enhance collision cross section differences and enable separation via trapped ion mobility spectrometry and time-of-flight mass spectrometry (TIMS–MS). The differential impact of alkali metal ion adduction (Na +, K +, Rb +, and Cs + ) on chiral separation was investigated, revealing that Na + adducts provide optimal enantioselective recognition (average resolution, R pp = 2.02). The established method achieved separation for 30 chiral amino acids, demonstrating faster speed, broader coverage, and superior resolution compared with existing approaches. When applied to rat blood samples from chlorfenapyr-induced toxicity models, the method successfully detected d -Arg, d -Ile, d -Leu, d -Phe, and d -Met, confirming its practical utility in toxicological diagnostics and biomarker discovery.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".