Effect of malva nut gum (purified and crude), sodium chloride and phosphate on cooking, texture, colour, rheology and microstructure of different chicken meat batters
Bibliographic record
Abstract
1. In the first experiment, the effect of adding purified malva nut gum (PMG) to comminuted poultry breast meat batters formulated with different contents of sodium chloride (NaCl; 10 to 30 g/kg) and tripolyphosphate (TPP; 0 and 5 g/kg) was studied. 2. Increasing salt (sodium chloride) content, along with the addition of 1 g/kg PMG, was beneficial in reducing cooking loss. At all salt contents, batters with PMG showed lower springiness than batters without PMG. Adding PMG to the batter with 20 g/kg salt and TPP decreased fracture force, springiness and chewiness. 3. In a second experiment, the effects of PMG (0.0, 3.0 and 6.0 g/kg), crude malva nut gum (CMG; 3.0 g/kg) and TPP (0.0 and 4.0 g/kg) on cooking loss, fat loss, colour, texture, rheology and microstructure of emulsified chicken meat batters were studied. 4. Increasing PMG reduced cooking and fat losses. Adding TPP increased hardness, springiness, cohesiveness and chewiness. The 1.0 g/kg PMG and TPP provided the greatest hardness. The batter with 3.0 g/kg PMG resulted in the lowest lightness (L*) and highest redness (a*). Adding PMG and TPP resulted in stable batters, as was evident by light microscopy results. The rheological evaluation showed the highest G' in the batter with 4.0 g/kg TPP followed in decreasing order by the batters containing TPP plus 3.0 g/kg PMG, TPP plus 1.0 g/kg PMG, 3.0 g/kg PMG, 1.0 g/kg PMG, 3.0 g/kg CMG and the control. 5. Overall, the results are important for developing new applications where malva nut gum can be used to improve yield and stability of meat products.
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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 | 0.001 | 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 | 0.001 |
| 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".