Development and validation of a headspace method for determination of furan in food
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the past furan had been found to form in foods during thermal processing. These findings and a recent classification of furan as a possible human carcinogen prompted us to develop a simple isotope dilution method for its determination in foods. We also investigated effects of furan volatility, sample matrix and partitioning of furan between water and fat constituents of sample on the analytical determination of furan. The method is based on headspace sampling of a 2 ml vial containing 1 g of sample. For analysis, samples were spiked with d(4)-furan, homogenized in a blender at 0 degree C, with water if required, and sub-sampled to vials containing sodium sulphate. After equilibration at 30 degrees C, 50 microl of headspace was injected into the split/splitless injection port of a GC/MS (EI, SIM). The method is linear in the 0.4-1000 ng/g range with a limit of detection of 0.1 ng/g.
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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 it