3-D Imaging of Deep-Fat Fried Chicken Nuggets Breading Coating Using X-Ray Micro-CT
Bibliographic record
Abstract
Food coatings are used to add value to deep-fat fried foods and to control heat and mass transfer during frying. They impart special characteristics such as crispiness and flavor to fried products while they also form a barrier to moisture and fat transfer during frying. Development of structure during frying plays an important role in defining the performance of batter/bread coatings. Food structural properties such as porosity have been associated with fat uptake in fried foods. A good understanding of the microstructural properties is necessary in order to produce high quality fried foods. X-ray micro-computed tomography (X-ray micro-CT) is a unique technique for imaging food non-invasively, requiring no or minimal sample preparation and 3-D rendition of high resolution images. The technique is capable of providing morphological details under a natural environment, which gives some advantages over the conventional imaging techniques such as microscopy. Study on chicken nuggets provided useful information relating frying conditions to structural changes in the breading-batter coating using X-ray micro-CT technique. Chicken nuggets were fried at 180°C for different frying durations after which the products were scanned using X-ray micro-CT. Images were reconstructed and analyzed, and 2-D and 3-D renditions of the coating images confirmed porosity changes with frying time. Numerical slicing of the 3-D images with image analysis software showed the degree of interconnectivity of pores, pore shape and pore count under different conditions of frying. The effect of frying time on microstructural parameters is significant. X-ray micro-CT shows great prospect in characterizing microstructural properties of foods especially coated fried products. This technique could be used in optimizing mass transfer during deep-fat frying by providing quantitative information on structural properties such as porosity, pore size distribution and pore connectivity.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".