Quantitative Determination of Trans-Fatty Acids in Oils and Fats by Capillary Gas Chromatography: Results of a JOCS Collaborative Study
Bibliographic record
Abstract
Excessive intake of trans-fatty acids increases the risk of cardiovascular disease. Much attention is drawn to the consumption of trans-fatty acids worldwide, and regulations for trans-fatty acids are instituted in several countries. Precise and convenient methods for determination of trans-fatty acid level are required, but there is no standard method using capillary Gas Chromatography in Japan. Therefore, for the new standard method, collaborative studies were carried out. The results were as follows: 1) Heptadecanoate (C17:0 free fatty acid) was chosen for internal standard substance. 2) Two Gas Chromatography columns, SP2560 (100 m) column (100% cyanopropyl polysiloxane liquid phase) and TC-70 (60 m) column (70% cyanopropyl polysilphenylene-siloxane liquid phase), were examined in the collaborative studies. We measured the edible oil samples containing 2-45 g/100 g of trans-fatty acids, and trans-fatty acid contents were quantitatively the same with both columns. The range of reproducibility coefficient of variation were below 10%. 3) Fats and oils sampled were soybean, rapeseed, palm, palm kernel, beef tallow, pork fat and their hydrogenated forms, for which good peak resolution was achieved. From the above results, the technique evaluated in the present study was considered to be suitable for determination of the content of trans-fatty acids in fats and oils exclusive of fish oil and milk fat.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".