Effect of Cooking Conditions on Cooking Yield, Juiciness, Instrumental and Sensory Texture Properties of Chicken Breast Meat
Bibliographic record
Abstract
The aim of the research was to investigate the effect of baking and grilling temperatures and times on quality characteristics of chicken breast meat. Eight packs of Industrial skinless chicken breast meat samples were purchased, frozen and sliced into dimensions, thawed and cooked by baking (BK) and grilling (GR) at 170, 180 and 190 oC for 0, 4, 8, 12 and 16 min. The cooking yield and loss were assessed by weight changes before and after cooking, juiciness, instrumental and sensory texture changes were investigated using 3 x 2 x 5 factorial experiment in complete randomized design (CRD). Relationships between changes in instrumental and sensory textures were evaluated. The cooking yield decreased significantly (p <0.05) and ranged from 67.99% to 70.90%, while cooking losses increased significantly (p <0.05) and ranged from 28.71% to 31.48%. Cooking decreased significantly (p < 0.05) mechanical properties of juiciness from 41.65% to 24.53%, but increased significantly (p < 0.05) hardness of chicken breast meat from 6.79 N to 11.33 N. The sensory texture showed that samples cooked by BK had higher textural score of 4.72 than GR with 4.52, but both cooking methods were rated neither crispy nor soft by the panelists. It was found that instrumental texture correlated positively with sensory texture in cooked chicken breast meat while sensory texture reversed with instrumental texture with respect to cooking parameters. Considering the overall quality indices evaluated, baking is the best cooking method at 170 oC for 8 min.
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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 | 0.001 |
| 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".