Characterisation of pore properties of deep‐fat‐fried chicken nuggets breading coating using mercury intrusion porosimetry technique
Bibliographic record
Abstract
Summary The objective of this study was to characterise the pore properties of deep‐fat‐fried chicken nuggets coating under different processing conditions namely frying temperatures (170, 180 and 190 °C) and time (0–240 s) using porosimetry technique. Porosity range obtained was between 39.93 and 68.99%. Porosity of the freeze‐dried samples decreased with frying time. The main effect of temperature on porosity was significant (P < 0.05). Porosity showed a high positive and negative correlation with moisture and fat contents, and the correlation coefficients ranged between 0.88 and 0.96 and 0.78 and 0.8, respectively. Bulk density increased with frying time, while apparent density was relatively the same. Pore distribution showed bimodality. There was no significant effect of temperature on pore size distribution. Over 70% of the pore volume is made up of pores greater than 1 μm. Pore volume ranged between 0.54 and 1.5 cm3 g−1, and it decreased with frying time. Mean pore diameter was between 0.006 and 389 μm, while with frying time, it ranged between 0.25 and 8.32 μm. Total pore area was between 2.53 and 16.53 m2 g−1. Hysteresis phenomenon showed that some of the pores were not perfectly cylindrical in shape.
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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".