Identification of the Maillard reaction intermediates as divalent iron complexes in alanine/glucose/FeCl2 model system using ESI/qTOF/MS/MS and isotope labelling technique
Bibliographic record
Abstract
Due to their high reactivities and short half-lives, the detection of Maillard reaction intermediates is relatively difficult to achieve in a single analytical run. In this study, the formation of Maillard reaction intermediates from heated alanine/glucose mixtures (110 °C for 2 h) was investigated through their complexation with divalent iron using electrospray ionization/quadrupole time-of-flight mass spectrometry and isotope labeling techniques. Analysis of the mixtures indicated that this approach allows the simultaneous detection of many important labile and reactive Maillard reaction intermediates along with unreacted alanine and glucose in addition to various other Maillard reaction products, such as glyceraldehyde, erythrose, ribose, acetol, glycolaldehyde, fructosamine, glucosone, osones, deoxyosones, and Amadori products. Some osones and deoxyosones also formed their corresponding Schiff bases with alanine. The above mentioned Maillard reactions intermediates were detected either as binary metal complexes with alanine or with other enediol generating species including self-complexation adducts and they formed positively charged ions such as [M + H]+, [M + Na]+, [M + K]+, [M + Fe35Cl]+, and [M + Fe37Cl]+, that can be detected using the positive ionization mode.
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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) | 0.001 | 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".