Why Asparagine Needs Carbohydrates To Generate Acrylamide
Bibliographic record
Abstract
Structural considerations dictate that asparagine alone may be converted thermally into acrylamide through decarboxylation and deamination reactions. However, the main product of the thermal decomposition of asparagine was maleimide, mainly due to the fast intramolecular cyclization reaction that prevents the formation of acrylamide. On the other hand, asparagine, in the presence of reducing sugars, was able to generate acrylamide in addition to maleimide. Model reactions were performed using FTIR analysis, and labeling studies were carried out using pyrolysis-GC/MS as an integrated reaction, separation, and identification system to investigate the role of reducing sugars. The data have indicated that a decarboxylated Amadori product of asparagine with reducing sugars is the key precursor of acrylamide. Furthermore, the decarboxylated Amadori product can be formed under mild conditions through the intramolecular cyclization of the initial Schiff base and formation of oxazolidin-5-one. The low-energy decarboxylation of this intermediate makes it possible to bypass the cyclization reaction, which is in competition with thermally induced decarboxylation, and hence promote the formation of acrylamide in carbohydrate/asparagine mixtures. Although the decarboxylated Amadori compound can be formed under mild conditions, it requires elevated temperatures to cleave the carbon-nitrogen covalent bond and produce acrylamide.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".