Factors Influencing Gel Formation by Myofibrillar Proteins in Muscle Foods
Bibliographic record
Abstract
Abstract: Considerable research has been done to better understand the basis for gel formation by myofibrillar proteins (MPs) in effort to manufacture acceptable processed meats with lower cost and more desirable nutritional characteristics. Results from research available indicate that there is no substitute for the myofibrillar protein myosin in gel formation by proteins from a wide variety of animal and fish species. This report consolidates information on determinants of protein gel formation, examining types of muscles and fibers, the species influence, and interactions of the MPs actin and myosin with each other and with fat, gelatin, starch, hydrocolloids, some protein soy, whey, and nonprotein additives such as phosphates and acidifiers, and the influences of pH, ionic strength, rates of heating, and its absence, protein oxidation, as well as the use of transglutaminase and high hydrostatic pressure. It is of interest that myosin alone will form acceptable gels. Gel formation by MPs is optimized at pH 6, an ionic strength of 0.6 M, and at 60 to 70 °C. The observations that collagen‐derived gelatin can reduce the rubbery texture of low‐fat products and that solubilization of MPs is not always essential for gel formation, and the observation that good gels can be formed in the absence of salt, are exciting developments that should be considered as pressure mounts to continue to reduce fat and salt in the diet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".