Influences of Operating Parameters on the Formation of Furan During Heating Based on Models of Polyunsaturated Fatty Acids
Bibliographic record
Abstract
Furan, a possible carcinogen, is commonly produced by thermal processing in a number of heated foods. The existence of furan levels in foods has attracted considerable attention worldwide. Recent research of furan in food has focused on the possible influences of operating parameters on the furan formation during heat processing. The aim of our study was to investigate the impacts of multiple factors (pH, temperature, heating time, ferric, and glutamic acid) on furan formation using linolenic and linoleic acids-based model systems in which furan was analyzed by headspace gas chromatography-mass spectrometry (HS-GC-MS). The results revealed that the content of furan increased rapidly when the heating temperature was elevated, with the highest levels of furan in neutral buffer solutions, the furan levels were also found to be related to heating time in all model systems. Ferric promoted furan formation from polyunsaturated fatty acids, conversely glutamic acid with an optimum concentration suppressed the furan formation. The minimal level of furan in foods during thermal treatment could be achieved via adding furan formation suppressors, and/or avoidance of furan forming promoter.
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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 | 0.000 | 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) | 0.001 | 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".