EFFECT OF THERMAL PRETREATMENT AND BATTER COMPOSITION ON FAT ABSORPTION IN DEEP-FAT FRIED BATTER
Bibliographic record
Abstract
The effects of batter composition, thermal pretreatment and frying time on mass transfer during deep-fat frying were studied using response surface methodology. Prior to frying, dough composed of wheat and rice flour mixtures (wheat/rice ratio between 0 and 100%) mixed with water at ratio 1:1.3 (mixture:water) were put in closed aluminum cells and preheated in a water bath at temperatures ranging from 60 to 90°C. Then, the samples were fried for 1, 2, 3 and 4 min. A central composite rotatable design was built to study the effects on fat uptake and moisture content of samples. Pre- gelatinization and batter formulation had impact on the degree of fat uptake in deep-fat fried batter coating. The results showed that flour ratios had more impact on fat uptake than pretreatment temperature. Moreover, the interaction between these 2 factors was more significant than their main effects. Batter composed of 75% wheat flour and pre- treated at a temperature of 82.5°C had the minimum optimal points for fat uptake with a value of 3.3% after 4 minutes of frying. The degree of gelatinization and the moisture content under these conditions were 80.55% and 30 % db, respectively.
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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".