Quantitative analysis of microcystin variants by capillary electrophoresis mass spectrometry with dynamic pH barrage junction focusing
Bibliographic record
Abstract
Dynamic pH junction is an online focusing method in CE based on the electrophoretic mobility difference of analytes in the sample matrix and the background electrolyte. An advantage of this method over the conventional CE is that the sensitivity can be significantly improved. By injecting a long sample plug in the capillary and focusing the analytes at the pH boundary between the background electrolyte and sample matrix, the LOD can be improved by 10-100 folds. The dynamic pH junction method can be easily coupled with ESI-MS. In this work, we used this method for the analysis of microcystins (MCs). The detection limits and dynamic ranges were studied. The separation was optimized by adjusting the injection time, and concentrations and pH values of the background electrolyte. The optimization of analyte focusing leads to enhanced detection response compared to conventional injections, achieving 200-400 fold higher averaged peak heights for four microcystin (MC) variants. More importantly, this method was successfully used for the quantitative analysis of microcystins (MCs) in crude algae samples from natural water bodies, making it promising for practical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 0.000 |
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".