Chemical properties and reactive oxygen and nitrogen species quenching activities of dry sugar–amino acid maillard reaction mixtures exposed to baking temperatures
Bibliographic record
Abstract
Maillard reaction products (MRPs) derived from 10 different, dry sugar-amino acid reaction model systems were examined for changes in color index (E), sugar loss, and formation of α-dicarbonyl compounds; the changes were correlated with relative activities to quench both reactive oxygen (ROS) and reactive nitrogen (RNS) species. Reducing sugars, xylose, ribose, fructose, glucose, and non-reducing sucrose were reacted with glycine (Xyl-Gly, Rib-Gly, Fru-Gly, Glc-Gly, and Suc-Gly), or lysine (Xyl-Lys, Rib-Lys, Fru-Lys, Glc-Lys, and Suc-Lys), respectively, at temperatures of 150°C and 180°C for time periods ranging from 5 to 60min. ROS quenching capacity was negatively correlated with color index (E) (r=-0.604, P<0.001), and positively correlated with sugar loss (r=0.567, P<0.001). MRPs also exhibited activity to quench RNS as assessed by nitric oxide (NO) inhibition in differentiated Caco-2 cells that were induced with interferon-γ (IFN-γ) and phorbol ester (PMA) cocktail. We also showed a correlation between RNS and color index, sugar loss, and ROS quenching activities for MR mixtures that were heated for a short time (e.g. 10min) at 150°C. MRP quenching of ROS was largely influenced by sugar type, whereas, RNS quenching was dependent more so on the interaction between reactants and reaction conditions used to generate MRPs.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".