Functional and rheological properties of proteins in frozen turkey breast meat with different ultimate pH
Bibliographic record
Abstract
Functional and rheological properties of proteins from frozen turkey breast meat with different ultimate pH at 24 h postmortem (pH(24)) have been studied. Sixteen breast fillets from Hybrid Tom turkeys were initially selected based on lightness (L*) values for each color group (pale, normal, and dark), with a total of 48 breast fillets. Further selection of 8 breast samples was made within each class of meat according to the pH(24). The average L* and pH values of the samples were within the following range: pale (L* >52; pH ≤5.7), normal (46 < L* < 52; 5.9 < pH <6.1), and dark (L* <46; pH ≥6.3), referred to as low, normal, and high pH meat, respectively. Ultimate pH did not cause major changes in the emulsifying and foaming properties of the extracted sarcoplasmic and myofibrillar proteins. An SDS-PAGE profile of proteins from low and normal pH meat was similar, which revealed that the extent of protein denaturation was the same. Low pH meat had the lowest water-holding capacity compared with normal and high pH meat as shown by the increase in cooking loss, which can be explained by factors other than protein denaturation. Gel strength analysis and folding test revealed that gel-forming ability was better for high pH meat compared with low and normal pH meat.Dynamic viscoelastic behavior showed that myosin denaturation temperature was independent of pH(24). Normal and high pH meat had similar hardness, springiness, and chewiness values as revealed by texture profile analysis. The results from this study indicate that high pH meat had similar or better functional properties than normal pH meat. Therefore, high pH meat is suitable for further processed products, whereas low pH meat may need additional treatment or ingredient formulations to improve its functionality.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".