Identification\nof Conjugated Linoleic Acid (CLA) Isomers\nby Silver Ion-Liquid Chromatography/In-line Ozonolysis/Mass Spectrometry\n(Ag<sup>+</sup>‑LC/O<sub>3</sub>‑MS)
Bibliographic record
Abstract
A novel method for the identification\nof conjugated linoleic acid\n(CLA) isomers has been developed in which silver ion liquid chromatography\nis coupled to in-line ozonolysis/mass spectrometry (Ag<sup>+</sup>-LC/O<sub>3</sub>-MS). The mobile phase containing CLA isomers eluting\nfrom the Ag<sup>+</sup>-LC column flows through a length of gas-permeable\ntubing within an ozone rich environment. Ozone penetrating the tubing\nwall reacts with the conjugated double bonds forming ozonolysis product\naldehydes. These, and their corresponding methanol loss fragment ions\nformed within the atmospheric pressure photoionization (APPI) source,\nwere detected by in-line MS and used for the direct assignment of\ndouble bond positions. Assignment of positional isomers is based entirely\non the two pairs of diagnostic ions seen in the in-line O<sub>3</sub>-MS mass spectra. In this way, de novo identification of CLA positional\nisomers, i.e. without requiring comparison to CLA standards, was achieved.\nThe Ag<sup>+</sup>-LC/O<sub>3</sub>-MS method was applied to the analysis\nof CLA isomers in a commercial CLA supplement, milk fat, and the lipid\nextract from a <i>Lactobacillus plantarum</i> TMW1460 culture.\nThe results demonstrate how Ag<sup>+</sup>-LC/O<sub>3</sub>-MS can\nbe used for the direct and fast determination of CLA isomers at low\nconcentrations and in complex lipid mixtures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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; both teacher heads agree on what is shown here.
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".