Alkali-aided protein extraction of chicken dark meat: Composition and stability to lipid oxidation of the recovered proteins
Bibliographic record
Abstract
Chicken dark meat has been considered as a major underused commodity due to the increasing demand for further-processed breast meat products. One option to increase the utilization of chicken dark meat is to extract myofibrillar proteins and separate them from fat and pigments to enhance their application for the preparation of further-processed meat products. The objective of the current study was to determine the effect of pH, in the range of 10.5 to 12.0, on the alkaline solubilization process of chicken dark meat. Aspects studied were the effect of the alkali-aided process on protein content, lipid composition, lipid oxidation, and color characteristics of the extracted meat. Each experiment and each assay were done at least in triplicate. Lipid content of the extracted meat showed a 50% reduction compared with the chicken dark meat. Neutral lipids were reduced by 61.51%, whereas polar lipids were not affected by the alkali treatments. There was a higher amount of TBA reactive substances observed in the extracted meat compared with chicken dark meat, indicating that extracted meat was more susceptible to oxidation. Long-chain polyunsaturated fatty acids (22:4n-6, 20:3n-3, 20:5n-3, 22:5n-3, and 22:6n-3), which were detected only in the polar lipids, were responsible for increasing lipid oxidation susceptibility of extracted meat compared with chicken dark meat. Alkali-aided extraction of chicken dark meat lightened the color of the meat. The redness, yellowness, and total heme pigments in extracted meat significantly decreased by 83, 11, and 53%, respectively, compared with chicken dark meat. Even though this process did not remove polar lipids, based on our early findings, the extracted meat had considerable physicochemical and textural properties for product preparation compared with those of raw dark meat. Hence, alkali recovery of protein can be considered a potentially useful method to increase the utilization of dark chicken meat.
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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 | 0.000 |
| 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".