Indole: A Promising Scavenging Agent for Methylglyoxal and Related Carbonyls in Tryptophan Containing Maillard Model Systems
Bibliographic record
Abstract
In situ generation of efficient carbonyl trapping agents from amino acids during food processing can be considered a useful approach to control the accumulation of harmful Maillard reaction products in food. Tryptophan is one such amino acid that can be used to generate carbonyl trapping agents. Indole, the main thermal degradation product of tryptophan, is known to react with simple aldehydes through electrophilic aromatic substitution type reactions mainly at carbon positions 2 and 3 in addition to the ring nitrogen. The ability of indole to scavenge three moles of reactive aldehydes per mole of indole such as formaldehyde, methylglyoxal, and phenylacetaldehyde was investigated using model systems containing tryptophan or indole. The model systems were either (a) heated in an aqueous solution in stainless steel reactors at specified time and temperatures and analyzed by qTOF-MS/MS or (b) directly pyrolyzed and analyzed by GC/MS using isotope labeling technique. Unlike the other aldehydes, the initial alcohol formed with phenylacetaldehyde was able to dehydrate and form an stable conjugated system with the indole. In general, indole was able to capture three moles of paraformaldehyde, three moles of methylglyoxal and three moles of phenylacetaldehyde and suppress the formation of 2-amino-1-methyl-6-phenylimidazo(4,5-b)pyridine (PhIP) generated in a model system.
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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) | 0.000 | 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".