Investigation of low molecular weight peptides (<1 kDa) in chicken meat and their contribution to meat flavor formation
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Bibliographic record
Abstract
BACKGROUND: Low molecular weight peptides (LMWPs) (<1 kDa) generated in meat during chilled conditioning can act as flavor precursors in the Maillard reaction with a potential contribution to key volatile organic compound (VOC) formation upon heating. Liquid chromatography/quadrupole time-of-flight mass spectrometry (LC/QTOF-MS) successfully detected 44 LMWPs in chicken breast and thigh muscles stored at 4 °C for up to 6 days. Carnosine (350 mg per 100 g), glutathione (GSH, 20 mg per 100 g) (concentrations based on reported values in the literature) and cysteine glycine (Cys Gly, 5 mg per 100 g) (concentration based on results from LC/QTOF-MS) were used in model systems containing ribose (25 mg per 100 g). The three model systems were heated at 180 °C for 2 h at pH 6.3. VOCs were measured by simultaneous distillation solvent extraction/gas chromatography/mass spectrometry. RESULTS: Of 33 VOCs detected, 26 were significantly different (P ≤ 0.05) between the three peptides. The majority of nitrogen-containing volatiles, pyrazines and pyridines, dominated the carnosine mixture, while sulfur-containing VOCs dominated the GSH and Cys Gly peptide mixtures. CONCLUSION: Known key aroma compounds such as thiazole (meaty), 2-methyl-3-furanthiol (beef and meat), 2-furfurylthiol (roasted), dihydro-2-methyl-3(2H)-thiophenone (meaty), 2-acetylthiazole (meaty and roasted) and pyrazine (meaty) were detected under conditions specific to aged and thermally treated chicken, suggesting a potential contribution to the overall sensory quality of cooked meat. © 2018 Society of Chemical Industry.
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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 | 0.000 | 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) | 0.000 | 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