Influence of hydrocolloids and flours on acoustic-mechanical and microstructural properties of battered deep-fat fried meat
Bibliographic record
Abstract
Abstract This study investigated the influence of hydrocolloid–flour mixtures on textural, structural, oil absorption and optical characteristics of batter-coated fried meat. The batter coatings were formulated with corn, wheat and rice flours along with different hydrocolloids (methylcellulose, carboxyl methylcellulose, xanthan gum, locust bean gum, gum arabic). Textural and structural properties were assessed by acoustic-mechanical and scanning electron microscopy, respectively. Results showed that the addition of hydrocolloids improved crispiness and reduced oil content in fried products. Comparative performances of the hydrocolloids in improving crispiness were in decreasing order as gum arabic > locust bean > xanthan gum > carboxyl methylcellulose > methylcellulose. Textural properties of the hydrocolloids were interwoven with the flour type, where corn flour demonstrating better crispiness than wheat and rice flours. The crispiness of hydrocolloids added batter increased up to seven times than the control samples. The hydrocolloids were found to significantly influence (P ≤ 0.05) the appearance of battered meat formulated with corn and wheat flours. Hydrocolloids showed prominent effect on wheat flour-based batter in terms of average pore area, whereas highest impact on corn flour regarding the number of pores formed.
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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".