Relationship between Oil Uptake and Moisture Loss during Deep Fat Frying of Deboned Chicken Breast Meat
Bibliographic record
Abstract
The relationship between moisture loss and oil uptake at different combination of Frying Oil Temperatures (FOT) and time during deep-fat frying of chicken meat was investigated in this study. Chicken meat samples were diced and fried at different FOT (170°C, 180°C, and 190°C) in an industrial fryer for periods varying from 5 to 900 s. Fat analysis was accomplished in a soxhlet extraction apparatus with petroleum ether solvent. Prior to fat analysis samples were freeze dried and the moisture analysis was based on the standard AOAC standard method. The results show that a cook value of 415 s was found to give the most ideal sensory characteristics. The relationship between moisture loss and oil uptake during the initial phase of frying (°C and 190°C, respectively, and the rate (1.35 g/s) at FOT 170°C was significantly (P < 0.05) lower.
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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".