Ion suppression : A major concern in mass spectrometry
Bibliographic record
Abstract
Ion suppression is one form of matrix effect that liquid chromatography–mass spectrometry (LC–MS) techniques suffer from, regardless of the sensitivity or selectivity of the mass analyzer used. Ion suppression negatively affects several analytical figures of merit, such as detection capability, precision, and accuracy. The limited knowledge of the origin and mechanism of ion suppression makes this problem difficult to solve in many cases. Over the past decade and a half since the response-reducing phenomenon was exposed, however, protocols have been developed not only to test for its presence but also to account for its effects and eliminate the risk of ion suppression altogether. Because there is no universal solution for the matrix effect, some of the viable options are discussed briefly in this tutorial, which alone or in combination can help regain the quality of LC–MS analysis for the particular matrix–analyte combination. Two commonly used techniques to detect the presence of the matrix effect are illustrated. Modifying instrumental components and parameters, chromatographic separation, and sample preparation are all considered as means of reducing or possibly eliminating ion suppression. A variety of calibration techniques for compensating the effects of the phenomenon also are discussed.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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".