Effect of the wheat starch/wheat protein ratio in a batter on fat absorption and quality attributes of fried battered and breaded fish nuggets
Bibliographic record
Abstract
Abstract Battered and breaded fish nuggets (BBFNs) were prepared by treating fish with a batter composed of wheat starch (WS) and wheat protein (WP) blends (at the ratios of 15:1, 13:1, 11:1, 9:1, and 7:1, w/w), frying at 170 °C (40 s) followed by 190 °C (30 s). Fried BBFNs were evaluated for moisture and fat contents, color, shrinkage, acrylamide content, and fat distribution. Results showed that moisture content and brightness ( L * value from colorimetry) increased with a decrease of WS/WP ratio to 11:1 w/w, then decreased as WS/WP ratio further decreased, while fat content, fat distribution level, and shrinkage of fried BBFNs presented opposite results. However, there was a slight influence of WS/WP ratio on yellowness ( b * value), redness ( a * value), and acrylamide content of fried BBFNs. Among WS/WP ratios, fried BBFNs with 11:1 w/w have the highest moisture content (16.43%) and the lowest fat content (23.39%), fat distribution level, shrinkage (10.72%), and acrylamide content (57 mg/kg), while a crust with golden‐yellow color was observed. This study demonstrates that moisture evaporation and fat absorption were significantly influenced by WS/WP ratio in the batter ( P < 0.05), with the most effective results in quality attributes improvement of fried BBFNs. Practical Application This study clearly showed that the fat content and quality attributes of fried BBFNs were significantly affected by WS/WP ratio in the batter ( P < 0.05). The inhibition of fat absorption and improvement of shrinkage and color in fried BBFNs was the most effective for a 11:1 w/w WS/WP ratio.
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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".