Effect of Different Frying Methods on Cooking Yield, Tenderness and Sensory Properties of Chicken Breast Meat
Bibliographic record
Abstract
This paper focused on the effect of different frying methods on the quality of chicken breast meat. Fresh boned broiler chicken breast meat samples were purchased, frozen, sliced into dimensions. These samples were cooked by air frying (AF) and deep fat frying (DF) methods at 170°C, 180°C and 190°C for 4, 8, 12- and 16-min. Cooking yield and loss were assessed by weight changes before and after frying and tenderness changes were determined by measuring the compression force using instrumental texture profile analysis (TPA). The sensory acceptance and preferences were conducted on the samples by panel of judges. Cooking yield of fried chicken breast meat decreased significantly (p < 0.05) with increasing frying temperature and time. Air fried (AF) samples had higher mean cooking yield value of 59.26 % than DF method sample of 50.00%. Samples fried at lower frying times had significantly (p < 0.05) higher cooking yield compared with longer frying times. Cooking loss increased significantly (p < 0.05) with increasing frying temperature and time. Samples fried with hot air adopting AF method had lower average cooking loss (40.20%), fat content (6.62 %) and higher compression force (hardness) value (12.39 kg/F) than samples fried by DF method which had higher cooking loss (49.47 %) and lower compression force or hardness (12.18 kg/F) and higher fat content (11.88 %). Samples fried for 4 min had significantly (p < 0.05) the least value in cooking loss and tenderness, but 8 min fried samples had better sensory attributes than 4 min fried samples, which were pinkish colour in appearance and unappetizing to consumers. Air frying method with the best tenderness value (20.43 ± 1.15 Kg/F), while deep fat frying method-produced samples with its best tenderness value (18.89 ± 0.70 Kg/ F) at 170°C for 16 min. Sensory evaluation showed that DF products were moderately crispy (7.19) compared to AF products, which were slightly crispy (5.45). The interaction effect of frying method, frying temperature and frying time was significant for cooking yield, loss and tenderness. However, the overall interaction (frying method x frying temperature x frying time) was found to be significant in coking yield and loss, but not significant in tenderness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".