MICROSTRUCTURAL CHARACTERIZATION OF DEEP‐FAT FRIED BREADED CHICKEN NUGGETS USING X‐RAY MICRO‐COMPUTED TOMOGRAPHY
Bibliographic record
Abstract
ABSTRACT X‐ray micro‐computed tomography imaging technique was applied to study microstructural characteristics of deep‐fat fried chicken nuggets. The results obtained showed a significant (P < 0.05) influence of frying conditions on microstructural properties of the deep‐fat fried breaded chicken nuggets. The porosity of the breading coating increased while that of the core remained relatively unchanged with frying time. The number of pores also increased with frying. The chicken nuggets coating and core pores showed a decreased interconnectivity after frying. The shapes of the samples' pores were between rod‐like and spherical structure. The pore size distribution for the coating and the core parts showed an increase in pores with diameter <100 µm in terms of volume and number count. Some correlations were observed between the sample's (coating and core) porosity and frying time, fat content and moisture loss. PRACTICAL APPLICATIONS Microstructural properties are critical in food quality assessment, product development and process optimization. The use of X‐ray micro‐computed tomography for food microstructural evaluation is relatively new and recent studies have shown that the technique has the potential to elucidate microstructural properties of food with greater details and resolution than some other imaging techniques such as microscopy. It gives images in 3‐D for better quantitative assessment, involves minimal sample preparation and allows scanning under native environment. This technique was used to study the microstructural properties of breading coating and chicken core parts of deep‐fat fried breaded chicken nuggets to obtain microstructural parameters that are useful in quality assessment and process modeling.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".