Origin and Mechanistic Pathways of Formation of the Parent FuranA Food Toxicant
Bibliographic record
Abstract
Studies performed on model systems using pyrolysis-GC-MS analysis and (13)C-labeled sugars and amino acids in addition to ascorbic acid have indicated that certain amino acids such as serine and cysteine can degrade and produce acetaldehyde and glycolaldehyde that can undergo aldol condensation to produce furan after cyclization and dehydration steps. Other amino acids such as aspartic acid, threonine, and alpha-alanine can degrade and produce only acetaldehyde and thus need sugars as a source of glycolaldehyde to generate furan. On the other hand, monosaccharides are also known to undergo degradation to produce both acetaldehyde and glycolaldehyde; however, (13)C-labeling studies have revealed that hexoses in general will mainly degrade into the following aldotetrose derivatives to produce the parent furan-aldotetrose itself, incorporating the C3-C4-C5-C6 carbon chain of glucose (70%); 2-deoxy-3-ketoaldotetrose; incorporating the C1-C2-C3-C4 carbon chain of glucose (15%); and 2-deoxyaldotetrose, incorporating the C2-C3-C4-C5 carbon chain of glucose (15%). Furthermore, it was also proposed that under nonoxidative conditions of pyrolysis, ascorbic acid can generate the 2-deoxyaldotetrose moiety, a direct precursor of the parent furan. In addition, 4-hydroxy-2-butenal-a known decomposition product of lipid peroxidation-was proposed as a precursor of furan originating from polyunsaturated fatty acids. Among the model systems studied, ascorbic acid had the highest potential to produce furan, followed by glycolaldehyde/alanine > erythrose > ribose/serine > sucrose/serine > fructose/serine > glucose/cysteine.
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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) | 0.001 | 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 | 0.001 |
| 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".