Contribution of two approaches using electrospray ionization with multi‐stage mass spectrometry for the characterization of linseed oil
Bibliographic record
Abstract
A detailed characterization of triacylglycerols (TAGs) present in linseed oil samples from a local producer was performed using electrospray ionization and two mass spectrometric approaches; direct infusion multi-stage mass spectrometry (MS(n)) experiments and liquid chromatography/tandem mass spectrometry (LC/MS/MS) using non-aqueous reversed-phase chromatographic conditions. The combination of both approaches permitted the identification of 26 TAGs. Comparison of the two analytical approaches showed that discrimination of regioisomers was achieved from MS3 data while other isobaric species were separated and identified by LC/MS/MS analysis. The results we obtained were also compared with those previously reported. The TAG composition of the studied linseed oil is qualitatively identical to that of linseed oils from various sources in Europe, Canada, Argentina or India. However, a few differences were observed with regard to the proportions of some TAGs; these can be explained by variations in the culture conditions, climate, and variety of the seeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".