Contribution of High‐Pressure‐Induced Protein Modifications to the Microenvironment and Functional Properties of Rabbit Meat Sausages
Bibliographic record
Abstract
Rabbit meat batters were subjected to high pressure (HP, 100 to 300 MPa for 3, 9, or 15 min) to elucidate their effects on proteins structures, the microenvironment, and the resulting functionalities of the subsequently heated products. To determine these effects, we investigated structural and microenvironmental changes using Raman spectroscopy and also expressible moisture content, textural characteristics, and dynamic rheological properties of batters during heating (20 to 80 °C). Untreated samples served as controls. Analysis of specific Raman spectral regions demonstrated that applications of HP to rabbit meat batters tended to induce the transformation of the all-gauche S-S conformation to gauche-gauche-trans in the batter system. HP treatment higher than 100 MPa for 9 min promoted secondary structural rearrangements, and molecular polarity enhancement in the proteins prior to cooking. Also, increases of O-H stretching intensities of rabbit meat sausages were obtained by HP treatment, denoting the strengthening of water-holding capacity. These HP-induced alterations resulted in improved texture and, perhaps, improved juiciness of rabbit meat sausages (P < 0.05), however they had relatively poorer rheological properties than the controls. Nevertheless, HP treatment, especially 200 MPa for 9 or 15 min, was an effective technique for improving the functionalities of gel-type products through modification of meat proteins.
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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".