Capillary electrophoresis‐mass spectrometry for analysis of complex samples
Bibliographic record
Abstract
CE features superior separation efficiency, small solvent consumption, as well as the ability to analyze most biomolecules with an open tube fused-silica column. When coupled with MS, the separation power of CE is enhanced by adding another separation dimension based on mass-to-charge ratios. CE-MS reduces the dependence on CE separation so that faster analysis can be achieved. It also yields higher sensitivity as well as the capability for analyte identification and structural elucidation. The use of CE-MS for biomolecule analysis has increased significantly in the last 5 years. New methods are being developed for large molecules, while analyses of smaller molecules are moving toward the study in more complex tissues and other matrices. In this article, the applications of CE-ESI-MS for complex samples in 2007-2011 are reviewed. The applications are categorized according to the types of analytes studied, including the analysis for proteins and peptides, carbohydrates, and small biomolecules. Sample preparation methods, coatings for capillary inner wall, online processing strategies, and other aspects are also reviewed in each category.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".