Regiospecific determination of short‐chain triacylglycerols in butterfat by normal‐phase HPLC with on‐line electrospray‐tandem mass spectrometry
Bibliographic record
Abstract
This study uses normal-phase HPLC with on-line positive ion electrospray mass spectrometry (ESI-MS) to obtain quantitative compositional data on both synthetic and butterfat short-chain TAG. The product ion tandem MS of standards averaged 11.1 times lower in abundance of the ion formed by cleavage of FA from the sn-2-position for the pairs of regioisomers in the TAG classes: L/L/S-L/S/L and L/S/S-S/L/S, where L denotes long and S short acyl chain (C2-C6). The molar correction factors, determined for 42 regioisomeric pairs of short-chain TAG of 20 randomized mixture of standards, differed by 1.4-80% as the ratios varied between 0.217 and 5.847. Butterfat TAG were resolved into four fractions on short flash chromatography grade silica gel columns. Pairs of regioisomers in the TAG classes L/S/S-S/L/S with predominance of L/S/S isomers and the sole regioisomers in the TAG classes L/L(M)/S were identified by tandem MS, where M denotes either 8:0 or 10:0 acyl chain. The total proportion of L/L(M)/S isomers was estimated at 34.7 and that of L/S/S-S/L/S at 1.0 mol%, including a small proportion of S/S/S. In contrast to previous work, the present data indicate the presence of a small proportion of butyric and caproic acids in the sn-1-position. The overall distribution of the FA in the short-chain TAG of butterfat, calculated from direct MS measurements, was consistent with the results of indirect determinations based on stereospecific analyses of total butterfat.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".