Effect of ultrasound pretreated hydrocolloid batters on quality attributes of fried chicken nuggets during post-fry holding
Bibliographic record
Abstract
In this study, batters formulated with different hydrocolloids (i.e., pectin, locust bean gum, xanthan gum, guar gum, hydroxypropyl methylcellulose and methylcellulose) were treated with ultrasound as edible coatings for fried chicken nuggets. Quality characteristics (i.e., batter pickup, flow behaviours, thermal properties, moisture loss, color and textural properties) in chicken nuggets coated with ultrasound treated batters were evaluated before and after post frying exposure to heat lamp. Ultrasonication significantly reduced batter pickup, flow behavior and gelatinization enthalpy, revealing its tendency to alter functional properties of batter systems. Rheological evaluation of all batter samples revealed a pseudoplastic (shear thinning) flow characteristic when fitted to power law model, with ultrasonicated (US) samples exhibiting a significantreduction in viscosity over non-ultrasonicated (NUS) samples. Compared to the control NUS, fat content of chicken nuggets coated with US-treated batters decreased by 39.0, 60.9, 62.87, 64.1, 65.7, and 65.0 % for pectin, locust bean gum, xanthan gum, guar gum, hydroxypropyl methylcellulose and methylcellulose, respectively. Finally, chicken nuggets coated with US and NUS treated batters exhibited greater cutting force values immediately after frying but declined within the first 10 min of heat lamp exposure and increased subsequently with extended heat lamp holding time. Furthermore, NUS-treated guar gum resulted in chicken nuggets with the most minimal variability in cutting force during post-frying holding, indicating that crispiness was maintained. Overall, application of ultrasound as a batter pretreatment technique can be exploited by the frying food industry as an alternative approach to producing low fat chicken nuggets with appreciable quality attributes.
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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) | 0.001 | 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".