Automated workflow utilizing saponification and improved epoxidation for the sensitive determination of mineral oil saturated and aromatic hydrocarbons in edible oils and fats
Bibliographic record
Abstract
Refined edible oils and fats are known to contain olefins resisting the typical epoxidation used for the sample preparation of mineral oil saturated and aromatic hydrocarbons (MOSH and MOAH). These olefins can be misinterpreted as MOAH and are therefore an important reason for inconsistent results between laboratories. Collaborative trials confirm this assumption for low MOAH contents near the quantitation limits regularly. In the scope of this work, a new epoxidation approach was developed. Persistent olefins in refined oils could be successfully epoxidized with performic acid. The reaction kinetics was investigated using model substances for biogenic olefins and MOAH. It was rationalized why certain olefins resist epoxidation and which MOAH can potentially get lost. A prominent peak cluster in the MOAH fraction of refined palm oils could be identified by means of GC-MS and explained why it cannot be epoxidized. Based upon this, an automated and streamlined workflow for sample preparation and analysis was composed tackling major problems identified in previously published methods. Optimized and miniaturized saponification, extraction, epoxidation, and enrichment paired with online LC-GC-FID led to a robust method that was tested and validated for edible oils and fats (RSDR < 7% for MOSH and MOAH at values of 14.9 and 2.1 mg/kg, respectively). Due to increased sample amount and minimized blank values, quantitation limits below 1 mg/kg for MOSH and MOAH were achieved. The trueness of the method was verified by analyzing collaborative trial samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".