Assessing textural changes of breaded deep-fat fried chicken nuggets during post-frying holdings under infrared heat-lamp using acoustic-mechanical techniques
Bibliographic record
Abstract
Abstract Texture is a multi-parameter attribute and one of the prime quality attributes of fried products. This study examined the influence of post-frying holdings under a heat lamp on the texture of breaded fried chicken nuggets. Chicken nuggets were deep-fat fried (2, 4, 6, and 8 min) in canola oil at 180 °C. The fried samples were kept under an infrared heat lamp for up to 1 h, and the textural characteristics were compared with samples not exposed to post-frying holdings. Textural characteristics were analysed through mechanical-acoustic approach. The mechanical-acoustic parameters related to crispiness analysed included maximum force (Fmax, N), number of force peaks (NFP), the area under force-deformation curves (AF, N.sec), sound pressure level (SPL, dB), number of sound peaks (AUX) and the area under the amplitude-time plots (AS, dB.sec). The mechanical parameters (Fmax: 19.11–48.96 N; NFP: 5–40; AF: 43–135 N.sec) of chicken nuggets increased with frying time, and these attributes were decreased during post-frying holding. Similarly, acoustic parameters (SPL: 64.75–90.67 dB; AUX: 180–795; AS: 63–98 dB.sec) of chicken nuggets increased with frying time, and these attributes were decreased during post-frying holding. Considering the studied parameters, 6 min of frying was found as optimal frying time. However, the post-frying infrared heat lamp treatment affected the mechanical textural parametres more than the acoustic parameters. Findings from this study will be useful to food industries in optimising textural retention during post-frying heat lamp holdings of 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 | 0.001 |
| 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".