Quantitative Analysis of TAG in Oils Using Lithium Cationization and Direct‐Infusion ESI Tandem Mass Spectrometry
Bibliographic record
Abstract
Abstract This study was undertaken to determine whether triple‐stage mass spectrometry (MS3) could be employed to obtain quantitative and regioisomeric data from complex oil samples without the need for a chromatographic step in the analysis protocol. Lithium‐7 trifluoroacetate and electrospray ionization were used to form lithium adducts of the triacylglycerols (TAG) in a fish oil sample. The first‐generation precursor ion was the lithium‐TAG adduct, the second‐generation precursor ion was formed by loss of a neutral acid side chain in the first fragmentation. The ions used for analysis were formed in the second fragmentation by loss of the lactones of the acid side chains remaining after the first fragmentation. This analysis scheme provided quantitative and regioisomeric data without interference from TAG in the sample other than TAG with the same acyl carbon number, one more double bond, and two acyl side chains in common with the analyte. Even in this case a majority of the interferences could be estimated and compensated. Analysis of synthetic samples containing the fish oil matrix indicated that both absolute and relative quantitative data could be obtained with average errors of approximately 5 %. The method proved well suited to routine analyses of complex oil samples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".