Changes in Temperature Profile, Texture and Color of Pork Loin Chop during Pan-frying
Bibliographic record
Abstract
The aim of this work was to investigate changes in thermal profile, meat texture and color of pork chop during pan-frying with or without one turnover. Pork chops (1.2 cm thick) were individually pan-fried at 175 ºC for 75 s without turnover or 150 s with one turnover. Internal temperature, meat color and texture at 11 designated positions were tracked. During frying, temperature and cooking loss significantly increased (P<0.001), accompanied by a significant increase (P<0.001) in hardness, gumminess, chewiness and L* value, together with a significant decrease (P<0.05) in springiness, cohesiveness, resilience and a* value. Positions close to the periphery generally showed significantly higher (P<0.05) final temperatures, and significantly lower (P<0.05) hardness, gumminess, chewiness, L* and a* values than those close to the center. These results could explain variation in tenderness within the same sample when evaluating eating quality of cooked meat.
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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".