Profiling of glucose degradation products through complexation with divalent metal ions coupled with ESI/qTOF/MS/MS analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sugar degradation products generated through thermal treatment of foods are considered the key precursors for various flavor compounds, toxicants and browning, but their high reactivity makes their detection difficult. In this study, a convenient analytical procedure for profiling of various reactive sugar intermediates having enediol or α-dicarbonyl moieties through complexation with divalent metal ions combined with electrospray ionization/quadrupole time-of-flight mass spectrometry was developed. Excess divalent iron chloride (FeCl2) was added to glucose or 13U6-[glucose] solutions in methanol either before or after heating at 110 °C for 2 h, and the samples were analyzed by tandem mass spectrometry. The results indicated the formation of ethylene glycol, glycolaldehyde, glyceraldehyde, glycerol, methylglyoxal, glyoxylic acid, erythrose, erythrosone, 3-deoxy-erythrosone, erythritol, ribose, ribosone, 3-deoxy-ribose, ribitol, 3-deoxy-glucosone, and rhamnose. These sugars and sugar degradation products acting as bidentate ligands were detected as positively charged mono- and bis-sugar iron complexes in the form of [M + H]+, [M + Na]+, [M + K]+, [M + Fe35Cl]+, and [M + Fe37Cl]+, as well as by charge localization on iron [M]+. The divalent metal complexation technique was applied for the profiling of sugar degradation products in aged manuka honey.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.018 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it