Porosity determination of deep‐fat‐fried coatings using pycnometer (Fried batter porosity determination by pycnometer)
Bibliographic record
Abstract
Summary The effect of processing conditions such as frying time and temperature, and batter formulation on pore development in deep‐fat fried chicken nuggets coatings were studied using helium pycnometer method. Chicken nuggets with preformed and laboratory prepared batter coatings were fried at temperatures between 170 and 190 °C for a time range between 0 and 240 s. There was significant (P < 0.05) effect of frying temperature and batter formulation on porosity. Porosity increased with frying time and temperature, and ranged between 2.15 and 47.92% for the preformed batter and 9.96 and 54.76% for the formulated batters. Apparent and bulk densities of the preformed batters increased and decreased with frying time, respectively, but both declined gradually with increasing frying temperature. As the level of rice flour in the formulation increased, apparent and bulk densities also increased. Batter formulation and frying temperature significantly (P < 0.05) influenced the variation in moisture and fat content of the fried batter. Porosity demonstrated positive and negative correlation with fat uptake and moisture loss, respectively, for all the batter coatings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".