Unravelling the nexus between structure, texture, and acoustic traits of fried chicken nuggets
Bibliographic record
Abstract
Abstract Texture is a multi-parameter attribute and one of the prime quality attributes of fried products. This study explored the nexus between microstructure and texture of fried breaded chicken nuggets. Chicken nuggets were deep-fat fried (2, 4, 6, and 8 min) in canola oil at different frying temperature (170, 180, and 190 °C). Microstructure and texture of fried samples were assessed by scanning electron microscopy (SEM) and acoustic-mechanical texture analyser, respectively. Maximum force (Fmax), number of force peaks (NFP), area under force-deformation plots (FA), sound pressure level (SPL), number of sound peaks (AUX), and area under amplitude-time curves (SA) were used to describe the textural parameters. Microstructural indices (porosity, number of pores, average pore area, polydispersity index, and average shape factor) were estimated from the SEM images. Results revealed that, both the frying time and temperature were positively correlated with the mechanical (Fmax: 25–55 N, NFP: 05–74 N, FA: 6500–15 000 N.sec) and acoustic (SPL: 88–97 dB, AUX: 650–810 dB, SA: 380–405 dB.sec) parameters. Frying time and temperature significantly (P < 0.05) impacted the formation of micropores in fried chicken nuggets. Crust microporosity showed strong positive correlations (r = 0.82) with the AUX value. The NFP, SPL, and SA of the fried nuggets were significantly (P < 0.05) impacted by the crust microporosity. Crust porosity (10–30%), number of pores (8–400), average pore area (10–4000 μm2), polydispersity index (0.05–0.2), and average shape factor (0.8–1.1) of fried nuggets were significantly (P < 0.05) influenced by both the frying time and temperature. Findings from this study would be useful in quality improvement and process monitoring including modelling of coated fried products.
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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) | 0.000 | 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".