Quality Changes in Nuggets and Frying Oils due to Repeated Deep Frying: A Comprehensive Study
Bibliographic record
Abstract
In this study, it was aimed to determine the effects of nuggets produced using turkey meat by repeated frying in olive oil under atmospheric and pressure frying conditions on some physicochemical and instrumental texture properties of nuggets and some quality properties (free fatty acid, peroxide value and total polar compound) of frying oil. While pressure frying increased the moisture value of the samples, the moisture values of the samples decreased as the number of frying repetitions increased. A higher TBARS value was determined in atmospheric frying compared to pressure frying (p˂0,01), and the TBARS value of the samples increased as the number of frying repetitions increased. Pressure frying increased the L* and b* values and decreased the a* value in the internal and external surface of the samples (p˂0,05). While the number of frying factor did not cause a significant difference (p˃0,05), on the color values in the internal surface, as the number of frying increased, the L* and b* values of the samples decreased and the a* values increased. While pressure frying process increased the hardness, resilience, cohesiveness, springiness, gumminess and chewiness values in nuggets (p˂0,01), frying method had no significant effect on adhesiveness (p˃0,05). It was determined that the frying method and the number of frying repetitions factors had a very significant effect (p˂0,01) on the free fatty acid, peroxide value and total polar substance content of the oils.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".