Effect of Cooking on Physicochemical and Microstructural Properties of Chicken Breast Meat
Bibliographic record
Abstract
The effect of cooking on pH, juiciness, instrumental colour and microstructural properties of chicken breast meat was investigated. Industrial skinless chicken breast meat samples were purchased, frozen and sliced into dimensions , thawed and cooked by air frying (AF), baking (BK), deep fat frying (DF) and grilling (GR) at 170, 180 and 1900C for 0, 4, 8, 12 and 16 min. The pH value of the cooked samples increased from 6.05 to 6.25. Cooking methods, temperatures and times each resulted to increase in pH. The results of objective sensory instrumental analyses showed that cooking decreased significantly (p < 0.05) juiciness of cooked chicken breast meat. Samples cooked by BK had the highest juiciness value of 24.91%, while DF cooked samples had the least value of 13.89%.The instrumental analyses increased L*, a*, b* values and browning index. The temperature and time of cooking showed similar effects on juiciness and instrumental colour. Short cooking time (8 min) and 1700C resulted in higher juiciness and best appetizing appearance to the consumers. The microstructure studies showed that raw chicken breast meat had an intact muscle fibres and bundles, but cooking caused disintegration of muscle fibres, perimysial – collagen shrinkage and it resulted to drier samples with big cracks/ voids and big surface damages, particularly in AF, BK and GR cooked products at 1900C 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".