Impact of Cooking Conditions on Proximate Composition and Textural Properties of Chicken Breast Meat
Bibliographic record
Abstract
The aim of this study was to evaluate the impact of cooking conditions on the proximate composition and textural properties (cohesiveness and chewiness) of chicken breast meat. Eight packs of industrial skinless chicken breast meat were cooked by air frying (AF), baking (BK), deep fat frying (DF) and grilling (GR) at 170, 180 and 190oC for 0, 4, 8, 12 and 16 min. The chicken breast packs were frozen and sliced into dimensions, thawed, cooked and analysed by a two way analysis of variance. The results revealed that cooking methods significantly (p < 0.05) decreased moisture and protein contents from 75.14 to 58.25% and 89.17 to 82.98%, but increased fat content from 4.26 to 7.78%, ash content from 1.95 to 2.39%, carbohydrate content from 4.63 to 6.95%, cohesiveness content from 0.40 to 0.52 and chewiness value from 3.63 to 6.05 kg. An increases in cooking temperatures and times decreased moisture content from 60.58 to % 56.34% and 75.14 to 47.40% and protein content from 83.77 to 82.11% and 89.17 to 79.45%. Similarly, increases in cooking temperatures and times significantly (p < 0.05) increased fat content from 7.00 to 8.44% and 4.26 to 10.12%, ash content from 2.15% to 2.59% and 1.95 to 2.67%. This study showed that increases in cooking temperatures decreased non-significantly (p > 0.05) carbohydrate content from 7.02 to 6.92%, but increases in cooking times increased carbohydrate content from 4.63 to 7.76%. An increases in cooking temperatures and times increased cohesiveness content from 0.50 to 0.54 and 0.40 to 0.63, chewiness value from 5.50 to 6.77 kg and 3.63 to 8.54 kg, respectively. There were no significant differences (p > 0.05) in chewiness values of samples cooked by AF and GR methods. The best cooking method/ temperature / time for low nutrient losses was BK, 170oC and 4 min.
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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 | 0.000 |
| 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 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".