Building a Bridge Between Ambient MS and LC‐MS by Non‐Exhaustive Microdesorption
Bibliographic record
Abstract
Ambient mass spectrometry (AMS) offers rapid screening but faces challenges in analyzing complex samples due to high matrix effects. The absence of a separation step can also lead to false positives due to the isomers or isobars. In this study, a sequential analysis strategy which combines ambient MS and LC-MS based on the non-exhaustive microdesorption in solid-phase microextraction (SPME) was developed for the first time. By combining coated blade spray (CBS)-MS with LC-MS, in the first step, a few microliters of solvent were used for non-exhaustive desorption with high enrichment factor for rapid screening by CBS-MS. For the suspicious samples, the remaining analytes on the SPME coating undergo exhaustive desorption, then followed by LC-MS confirmation. The matrix-compatible coating used in the SPME device significantly reduces matrix effects while enhancing sensitivity through analyte enrichment. This method is environmentally friendly, utilizing only a few microliters of organic solvents for screening. The approach was rigorously validated, both theoretically and experimentally, and successfully applied to anti-doping testing, enabling detection of 53 prohibited substances in urine samples by integrating CBS-MS with LC-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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".