Enhancing understanding of microstructure-texture relationship in doughnuts: A comparative study of deep-fat and hot-air frying
Bibliographic record
Abstract
This research evaluated the effects of frying temperature (150, 165, and 180 °C) and method (deep-fat and hot air) on doughnut crumb and texture changes. The findings indicated that the dough phase progressively transformed into crumb from the exterior to the interior, accompanied by significant alterations in pore size, shape, and color, as well as the composition of the dough phase. The conversion process duration was influenced by frying temperature and method, lasting approximately 1–1.5 min. The conversion rate was higher in the deep-fat method than in the hot-air one. Overall, textural changes occurred at a lower rate in the hot-air method, as the most changes took place during the first and second 30 s of the process in the deep-fat (180 °C) and hot-air methods, respectively. The doughnut microstructure was assessed using micro-computed tomography (Micro-CT) to study the process of changes in the product's wall thickness and pore size. Considering the front view of the doughnut dough Micro-CT images before frying, it can be asserted that a greater number of small pores were present in the vicinity of the product's surface. The current study's findings could provide a fundamental understanding of microstructure and texture development during doughnut frying, showing that the air frying could be an alternative method to make healthier foodstuff with less oil content while preserving more or less same texture properties. • This study compares deep-fat/hot-air frying on the microstructure and texture of doughnuts. • Micro-CT shows major changes in pore size/shape in doughnut during frying. • Hot-air frying offers healthier alternative to deep-fat frying for doughnut production.
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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 | 0.000 |
| 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 | 0.001 |
| 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".