The Role of Creatine in the Generation of N-Methylacrylamide: A New Toxicant in Cooked Meat
Bibliographic record
Abstract
Investigations of different sources of acrylamide formation in model systems consisting of amino acids and sugars have indicated the presence of two pathways of acrylamide generation; the main pathway specifically involves asparagine to directly produce acrylamide after a sugar-assisted decarboxylation step, and the second, nonspecific pathway involves the initial formation of acrylic acid from different sources and its subsequent interaction with ammonia and/or amines to produce acrylamide or its N-alkylated derivatives. Aspartic acid, beta-alanine, and carnosine were found to follow the acrylic acid pathway. Labeling studies using [(13)C-4]aspartic acid have confirmed the occurrence in this amino acid of a previously proposed sugar-assisted decarboxylation mechanism identified in the asparagine/glucose model system. In addition, creatine was found to be a good source of methylamine in model systems and was responsible for the formation of N-methylacrylamide through the acrylic acid pathway. Labeling studies using creatine (methyl-d(3)) and (15)NH(4)Cl have indicated that both the nitrogen and the methyl groups of methylamine had originated from creatine. Furthermore, analysis of cooked meat samples has also confirmed the formation of N-methylacrylamide during cooking.
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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 | 0.001 | 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".