The Influence of Particle Size and Protein Content in Particle-Filled Myofibrillar Protein Gels
Bibliographic record
Abstract
The addition of glass microspheres as a model insoluble, hydrophilic filler in comminuted meat gels was investigated. The influence of protein content (9, 11, and 13%), filler size (∼4 μm, 7 to 10 μm, and 30 to 50 μm), and volume fraction filler (ϕf) on the microstructure, cooking losses, and large deformation/textural properties were evaluated. Microstructural analysis indicated the glass microspheres did not strongly interact with the gel matrix. For all protein levels investigated, cooking losses decreased with increasing ϕf, and this impact was more pronounced with smaller filler particles. The textural attributes of the 9 and 11% protein gels exhibited a similar dependence on filler size. When incorporating the 4 μm and 7 to 10 μm particles at increasing ϕf, the Hardness, Resilience, Cohesiveness, and Springiness all displayed a sharp increase to a plateau. The larger 30 to 50 μm particles exhibited no increase in any of the textural properties until higher ϕf were employed. In the 13% protein gels, the influence of the particles were diminished, and the effect of particle size was substantially reduced. The influence of these insoluble model filler particles was attributed to their ability to decrease the mobility of the aqueous phase, which explains their minor impact on more stable formulations. Through this work, it has been demonstrated that micrometer-sized hydrophilic particles have the potential to improve the stability and enhance the textural properties of comminuted meat gels. These findings suggest that micrometer-sized inert particles might function as an effective stabilizer in comminuted meat batters at low concentrations.
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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) | 0.000 | 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 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".