Thermomechanical and physicochemical evolution of meat-analogue based fried foods
Bibliographic record
Abstract
Abstract This study aimed to get mechanistic insights on the evolution of major quality attributes of meat-analogue (MA) based batter-coated fried products. Wheat and rice flour-based batter systems were used to coat a MA. The products were deep-fried at 180°C for 4 min in canola oil and their post-fry quality changes at room environment (RE, 25°C) and under IR-heating (65 °C) were investigated. Results showed that in addition to moisture-fat profile, batter coating substantially (P < 0.05) influenced the development of thermal, textural, colour and microstructural traits of MA. Post-fry colour changes (ΔE value: 3.1 to 10.4) in MA-based coated product continued under IR-heating (65 °C). Evolution of the thermal, textural and colour attributes of MA-based coated products were significantly (P < 0.05) influenced by the formulation of outer batter coatings. Moisture migration occurred from high moisture (ranged from 1.07 to 1.25 g g−1 dry matter) containing core to dry (moisture ranged from 0.15 to 0.59 g g−1 dry matter) crust region of MA-based coated fried product, where the moisture migration was interlaced with batter formulations. Holding environment and duration substantially (P < 0.05) impacted the textural properties (hardness, brittleness and crispiness) of MA-based coated fried product. Glass transition temperature (Tg) of coated fried products were ranged between −20.4 to −23.0 °C, that explains quality changes at RE and under IR-heating. Scanning electron microscopic image analysis (surface openings: 5.7 to 27.31%; fractal dimension: 2.555 to 2.702) revealed the impact of surface microstructure and mass-transfer modulated textural development in MA-based coated products. FTIR spectroscopy revealed surface chemical profile in relation to colour evolution of MA-based fried products. Principal component analysis outcome (PC1: 52.9.% and PC2: 25.8%) revealed varying extent of correlation between studied thermomechanical and physicochemical attributes.
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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".