Liquid Chromatography–Light Scattering Detector–Mass Spectrometric Analysis of Digested Oxidized Rapeseed Oil
Bibliographic record
Abstract
Rapeseed oil was oxidized chemically and thermally to produce two distinct oxidized oils. These oils, along with unoxidized oils, were subjected to an artificial digestion model to simulate the digestive processes in humans. Lipid digestion involves lipases that break down the intact triacylglycerol (TAG) molecules first to diacylglycerols, and eventually to sn-2-monoacylglycerols (MAG) and free fatty acids. A high performance liquid chromatography-evaporative light scattering detector-electrospray ionization-mass spectrometric (HPLC-ELSD-ESI-MS) method was developed to monitor the lipolysis and the presence of oxidized lipids. The HPLC-ELSD-ESI-MS analysis enabled the separation and detection of nearly all the lipid species present in the sample after TAG hydrolysis. The HPLC-MS analyses of digestion products revealed that oxidized triacylglycerols are hydrolyzed by the digestive enzymes in a manner similar to that of native, unoxidized molecules. Significant amounts of sn-1(3)-MAG were found in all the samples after lipolysis, however, more of these were found in unoxidized rapeseed oil samples than in the oxidized oils. Several oxidized molecules were identified with the aid of synthesized oxylipids. This novel method is scalable to small-scale preparative fractionation of oxidized lipid molecules from a complex digestion sample. Also, the fingerprint-like, diagnostic, MS profiles of oxidized oils, reference compounds, and digestion products may be a great aid in comprehensive analysis of lipid oxidation and lipolysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".