Migration time correction for dual pressure capillary electrophoresis in semi‐targeted metabolomics study
Bibliographic record
Abstract
Migration time fluctuation strongly affects peak alignment and identification of unknown compounds, making migration time correction an essential step in capillary electrophoresis (CE)-based metabolomics. To obtain more reliable information, metabolites with different apparent mobilities are analyzed by tandem mass spectrometry. Applying a small pressure is a common practice for reducing the analysis time of anions in a positive mode CE, known as the pressure-assisted CE. However, applying pressure may reduce the separation efficiency and can be undesirable for cation analysis. A simple way to address this issue is to increase the pressure after a certain time, during the separation. We term this practice as dual pressure CE. However, changing the pressure during the CE separation complicates migration time correction. Previous migration time correction methods were established based on a consistent electroosmotic flow and a constant pressure-driven bulk-flow velocity. We proposed a new correction method to support the peak alignment when dual pressure CE is used. A Python-based script was developed to implement dual pressure CE migration time correction for semi-targeted metabolomics study performed by a multiple reaction monitoring-based method. This script can help select suitable endogenous metabolites as correction markers, perform migration time correction, and conduct peak alignment. A case study showed that migration time precision of 156 metabolites in 32 samples can be improved from 4.8 to 11.4%RSD (relative standard deviation) to less than 1.8%RSD.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.003 |
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".