Determining Ethyl Esters in Fish Oil with Solid Phase Microextraction and GC–MS
Bibliographic record
Abstract
Abstract The long‐chain polyunsaturated fatty acids (PUFA) found in fish oil, specifically eicosapentanoic acid (EPA) and docosahexanoic acid (DHA) play an important part in human health. As a result, fish oil supplements are commonly consumed by people around the world. Supplements in the form of triacylglycerols (TAG) can be sold at a premium price, compared to those in the ethyl ester (EE) forms. Producers of TAG supplements require a simple, rapid method to determine the authenticity of their raw material. Here, we describe a method to quantify EE in fish oil using solid phase microextraction headspace analysis and GCMS. Despite the variation in linear ranges of the calibration curves with volatility of the EE, 30 individual FA were quantified including common saturated FA such as palmitic and stearic acid, as well as longer chain PUFA, such as EPA and DHA. The method was then applied to three commercial fish oils in the TAG form and two of the products were found to contain EE, with one containing EE above 1.5% w/w, indicating that contamination had occurred. With growing consumer interest in fish oil products, the method proposed here will help resolve future issues of authenticity in fish oils.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".