Effect of regular and hydrolysed dairy proteins on texture, microstructure and colour of lean poultry meat batters
Bibliographic record
Abstract
Summary The use of 2% milk protein isolate (MPI), and some of its fractions which included caseinate, whey protein isolate (WPI), two whey protein hydrolysates (5.2% and 8.5%; WPH‐I and WPH‐II respectively) and β‐lactoglobulin (β‐lac) was evaluated in lean chicken breast meat batters. Adding caseinate and MPI resulted in the highest fracture force values, and caseinate also provided higher yield compared with the control. Both proteins were observed to form distinct protein islands embedded within the meat protein matrix, which appeared to enhance the gel structure. The two hydrolysates provided the highest yield compared with all other treatments. However, adding WPH‐II also resulted in the lowest fracture force and hardness values, while WPH‐I provided similar values to the control. The low hardness value could be explained by the light micrograph which showed WPH‐II interfering with the binding of the meat proteins. The WPI and β‐lac provided similar yield, fracture and hardness values as the control. The colour of the products was most affected by the WHP‐I and WHP‐II; both resulted in lower lightness, yellowness and overall spectra reflectance curves. A cost analysis revealed that caseinate addition was the most economical in this lean meat system.
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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".