Identification of Conjugated Linoleic Acid (CLA) Isomers by Silver Ion-Liquid Chromatography/In-line Ozonolysis/Mass Spectrometry (Ag<sup>+</sup>-LC/O<sub>3</sub>-MS)
Bibliographic record
Abstract
A novel method for the identification of conjugated linoleic acid (CLA) isomers has been developed in which silver ion liquid chromatography is coupled to in-line ozonolysis/mass spectrometry (Ag(+)-LC/O3-MS). The mobile phase containing CLA isomers eluting from the Ag(+)-LC column flows through a length of gas-permeable tubing within an ozone rich environment. Ozone penetrating the tubing wall reacts with the conjugated double bonds forming ozonolysis product aldehydes. These, and their corresponding methanol loss fragment ions formed within the atmospheric pressure photoionization (APPI) source, were detected by in-line MS and used for the direct assignment of double bond positions. Assignment of positional isomers is based entirely on the two pairs of diagnostic ions seen in the in-line O3-MS mass spectra. In this way, de novo identification of CLA positional isomers, i.e. without requiring comparison to CLA standards, was achieved. The Ag(+)-LC/O3-MS method was applied to the analysis of CLA isomers in a commercial CLA supplement, milk fat, and the lipid extract from a Lactobacillus plantarum TMW1460 culture. The results demonstrate how Ag(+)-LC/O3-MS can be used for the direct and fast determination of CLA isomers at low concentrations and in complex lipid mixtures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".