Mineral oil hydrocarbons in minimally processed nutraceutical oils
Bibliographic record
Abstract
The presence of unintended chemicals in food products and supplements may impact consumers’ health negatively. Mineral oil hydrocarbons (MOHs) in particular are gaining research attention and have been detected and quantified in food products and supplements in the past. The aim of this study was to analyze encapsulated, and bulk minimally processed marine oils for MOHs and to evaluate the probable sources of these compounds. Hydrocarbons in supplement oils were extracted via saponification and analyzed by gas chromatography with both flame ionization and mass spectral detection. While no mineral oil aromatic hydrocarbons (MOAH) were detected in any sample, the analysis revealed the presence of mineral oil saturated hydrocarbons (MOSH) in 9 out of 10 minimally processed encapsulated oils. The MOSH appeared on the chromatograms as an unresolved complex mixture (UCM) with concentrations ranging from 376 ± 49 to 3831 ± 414 mg kg-1. These values are well below the maximum allowable limits for MOH in encapsulated products set by the United States Food and Drug Administration. Therefore, all the tested products are compliant with the US regulations. Moreso, the bulk oil samples did not contain detectable levels of MOH. This study suggests that MOH accumulation in encapsulated products is likely due to the use of lubricants during encapsulation, rather than environmental sources such as oil spills since MOAH that are characteristic of weathered petroleum products were not identified in the UCM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".