Relative molar response of lipophilic marine algal toxins in liquid chromatography/electrospray ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: Accurate quantitative analysis of lipophilic toxins by liquid chromatography/mass spectrometry (LC/MS) requires calibration solution reference materials (RMs) for individual toxin analogs. Untargeted analysis is aimed at identifying a vast number of compounds and thus validation of fully quantitative untargeted methods is not feasible. However, a semi-quantitative approach allowing for profiling is still required and will be strengthened by knowledge of the relative molar response (RMR) of analogs in LC/MS with electrospray ionization (ESI). METHODS: RMR factors were evaluated for toxins from the okadaic acid (OA/DTXs), yessotoxin (YTX), pectenotoxin (PTX), azaspiracid (AZA) and cyclic imine (CI) toxin groups, in both solvent standards and environmental sample extracts. Since compound ionization and fragmentation influences the MS response of toxins, RMRs were assessed under different chromatographic conditions (gradient, isocratic) and MS acquisition modes (SIM, SRM, All-ion, target MS/MS) on low- and high-resolution mass spectrometers. RESULTS: In general, RMRs were not significantly impacted by chromatographic conditions (isocratic vs gradient), with the exception of DTX1. MS acquisition modes had a more significant impact, with PnTX-G and SPX differing notably. For a given toxin group, response factors were generally in the range of 0.5 to 2. The cyclic imines were an exception. CONCLUSIONS: Differences in RMRs between toxins of a same chemical base structure were not significant enough to indicate major issues for non-targeted semi-quantitative analysis, where there is limited or no availability of standards for many compounds, and where high degrees of accuracy are not required. Differences in RMRs should be considered when developing methods that use a standard of a single analogue to quantitate other toxins from the same group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.002 |
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".