Thermomechanical transitions of meat-analog based fried foods batter coating
Bibliographic record
Abstract
This study aimed to characterize the thermomechanical transitions of meat-analog (MA) based coated fried foods. Wheat and rice flour-based batters were used to coat the MA and fried at 180 °C in canola oil for 2, 4 and 6 min. Glass-transition-temperature (Tg) of the coatings were assessed by differential scanning calorimetry, directly after frying or after post-fry holding. Mechanical texture analyzer and X-ray microtomography were employed to assess textural attributes and internal microstructure, respectively. Batter-formulation substantially impacted the Tg of fried foods coating i.e., crust. Tg of fried foods crust were ranged between -20 °C to -24 °C. Tg was positively correlated with frying time and internal microporosity (%), whereas negatively correlated with moisture content. Internal microstructure greatly influenced the textural attributes (hardness, brittleness, crispiness). Post-fry textural stability considerably impacted by Tg. Negative Tg value explains post-fry textural changes (hard-to-soft, brittle-to-ductile, crispy-to-soggy) of MA-based coated products at room-temperature (25 °C) and under IR-heating (65 °C).
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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) | 0.004 | 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".