Recent advances in microstructure characterization of fried foods: Different frying techniques and process modeling
Bibliographic record
Abstract
Background Due to the increasing trend in consumer habits to use healthy food products with low fat content, reduction of oil uptake during different frying processes is necessary. Recent studies have clearly revealed that microstructural changes occurred during frying operations significantly impact oil uptake. These variations are assessed for better comprehension of the mechanisms involved in oil absorption of fried products to minimize oil uptake without sacrificing organoleptic and textural properties of the foods. Different strategies such as state-of-the-art computational simulations based on numerical analysis of simultaneous momentum, heat and mass transfer modeling during frying have been attempted by several researchers to better control the process. Scope and approach This review paper presents a comprehensive and up-to-date review of microstructure variations covering all existing methods of frying operations comprising deep-fat frying, vacuum frying, hot-air frying, non-fat frying and microwave frying together with post-frying treatments and process modeling of frying. Key findings and conclusions Oil uptake can be controlled during frying by proper process design regarding different products and frying operations. Textural and organoleptic characteristics of fried foods are affected by applying various frying processes. Microstructural changes and post-frying treatments influence oil uptake during frying. In addition, suitable design and optimization of frying using process modeling is important to produce fried food products with high quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".