Application of batter coating for modulating oil, texture and structure of fried foods: A review
Bibliographic record
Abstract
Food surface modulation by batter coating is a promising approach to reduce the presence of oil in fried products. This review critically discussed the functionalities, mechanism of actions, rheology, ingredients of formulation, mathematical modeling of the process, cooking method, safety and regulatory aspects, physicochemical, thermal-microstructural characterization of batter coatings, and future research directions. Enormous list of ingredients could be used in preparation of oil-reducing viscoelastic batter coating that includes mostly flours, hydrocolloids, and starches. Bioactive compounds, enzymes, minerals, herbal extracts, baking agents, sugar alcohols, etc. could be incorporated in batter formulation to affect the taste and texture of coated products. Overall mass-transfer process of batter-coated fried foods could be characterized by several mathematical models (Fick, Newton, Page, Henderson & Pabis, modified Page, Arrhenius). Surface and internal microstructural characterization techniques, thermal probing, physicochemical characterization techniques and artificial intelligence can characterize different functionalities of batter coatings including oil reduction and textural evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".