Nonlinear Signal Response in Electrospray Mass Spectrometry: Implications for Quantitation of Arsenobetaine Using Stable Isotope Labeling by Liquid Chromatography and Electrospray Orbitrap Mass Spectrometry
Bibliographic record
Abstract
Isotope amount ratio measurements by electrospray ionization mass spectrometry show large systematic biases. Moreover, the signal ratio response can vary nonlinearly with respect to the amount ratio depending on the concentration of the analyte or coeluting matrix components, among other things. Since isotope dilution relies inherently on the linearity of response, accurate quantitation is then more difficult to achieve. In this study, we outline a method to eliminate the quantitation errors due to the effects of the nonlinear signal response. The proposed approach is a hybrid of the method of standard additions and isotope dilution allowing correction for nonlinear trend. As a proof of concept, determination of arsenobetaine content in fish tissue was performed using liquid chromatography coupled with a linear quadrupole ion trap (LTQ) Orbitrap mass spectrometer. The nonlinear isotope dilution method could, in principle, be applied to correct isotope ratio measurement biases in popular relative quantitation methods of biomolecules such as stable isotope labeling by amino acids in cell culture (SILAC), isotope-coded affinity tag (ICAT), or isobaric tags for relative and absolute quantification (iTRAQ).
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".