Applicability of non‐linear versus linear fractional abundance calibration plots for the quantitative determination of triacylglycerol regioisomers by tandem mass spectrometry
Bibliographic record
Abstract
RATIONALE: Regioisomeric analysis of triacylglycerols is important in understanding lipid biochemistry and the involvement of lipids in disease and nutrition. The use of calibration plots employing fractional abundances provides a simple and rapid method for such analyses. These plots are believed to be linear, but evidence exists for non-linearity. The behavior of such plots needs to be understood to allow for proper interpretation of regioisomeric data. METHODS: Solutions of five regioisomer pairs were prepared from pure standards and used to construct calibration plots using triple-stage tandem mass spectrometry (MS(3) ) with electrospray ionization (ESIMS(3) ) and cationization by lithium ions. The data were taken by direct infusion with an AB SCIEX QTRAP 2000 QqLIT mass spectrometer. RESULTS: Non-linear calibration plots were observed for the four isomer pairs containing the polyunsaturated eicosapentaenoic (20:5) and docosahexaenoic (22:6) acids paired with palmitic acid (16:0) or myristic acid (14:0), while the pair including palmitic and stearic (18:0) acids provided a linear plot. A non-linear model was developed for these plots and then verified experimentally. CONCLUSIONS: The fractional abundance calibration plots used in regioisomeric analysis of triacylglycerols are intrinsically non-linear, but may appear linear if the scatter in data points obscures the curvature, if the curvature is slight, or if the response factors for the two isomers in the regioisomer pair are similar. Therefore, linearity should not be assumed for these types of measurements until confirmed experimentally.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".