Effect mechanism of capsaicin and dihydrocapsaicin in <i>chili</i> on the oxidative stability of myoglobin in duck meat
Bibliographic record
Abstract
Abstract BACKGROUND Myoglobin (Mb) in duck meat is commonly over‐oxidized when heated at high temperatures, which may worsen the color of the meat. Enhancing the oxidative stability of Mb is essential for improving the color of duck meat. Capsaicin and dihydrocapsaicin (CA‐DI) in chili exhibit antioxidant properties. This study investigated the effects of CA‐DI on the structure and oxidative damage of Mb by fluorescence spectroscopy, differential scanning calorimetry analysis and particle size in duck meat during heat treatment. RESULTS When the ratio of CA‐DI to Mb was 10:1 g kg−1 and heat‐treated for 36 min, oxymyoglobin significantly increased, and metmyoglobin significantly decreased compared with the control group (P < 0.05). In parallel, the carbonyl content of Mb in the CA‐DI group decreased by 43.40 ± 0.10%, the sulfhydryl content increased by 188 ± 0.21%, and the free radical scavenging activity of Mb was significantly enhanced (P < 0.05). Moreover, the addition of CA‐DI resulted in a significant decrease in the particle size of the Mb surface (P < 0.05). When the ratio of CA‐DI to Mb was 10:1 g kg−1, CA‐DI enhanced the thermal stability and significantly increased the thermal denaturation temperature of Mb. The molecular docking results indicated that hydrophobic interactions and hydrogen bonds were involved in the binding of CA‐DI to Mb. CONCLUSION CA‐DI could combine with Mb and improve the oxidation stability of Mb in duck meat. This suggested that CA‐DI could be a potential natural antioxidant that improves the color of meat products. © 2024 Society of Chemical Industry.
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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".