Microstructure, texture and colour development during crust formation on whole muscle chicken fillets
Bibliographic record
Abstract
1. The development of crust during a 22-min period was evaluated in an oven, and in previously cooked-in-bag products (no crust) placed in an oven for 10 min. The oven-roasted products started to develop a thin (2-4 μm) crust layer after 4 min. At that point, the colour of the fillets turned white but no browning was observed. As roasting time increased, crust thickness and shear force increased, the product turned brown and eventually black at certain spots. 2. Light microscopy revealed the shrinking of muscle fibres close to the surface, as they also lost water. At a certain point, tears between the different layers started to appear. The inner muscle fibres also progressively shrank and the spaces between them increased. Microscopy of cook-in-bag products revealed no crust formation during heating. Upon moving to the oven, crust started to form but was much faster compared with the other products. 3. Cook-in-the-bag samples showed a higher rate of cook loss during the first 12 min (to internal 70°C) compared with oven heating. This could have been due to the fast heating rate in water and/or no crust formation. 4. White colour was fully formed on water-cooked fillets within 2 min (L* = 83), while it was gradually forming on oven-roasted samples (max L* of 79 after 12 min). 5. Shear force measurements showed an increase in both treatments up to 18 min, with a decrease thereafter (when dry crust started to crack).
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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 | 0.000 |
| 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) | 0.001 | 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".