Evaluation of black bean flour as enrobing material on the quality characteristics of chicken nuggets
Bibliographic record
Abstract
The present study was undertaken to evaluate physico-chemical properties, viz., pH, cooking yield (%), moisture (%), crude protein (%), ether extract (%), ash (%), crude fiber (%), moisture-protein ratio, and coating thickness (cm), and sensory attributes, viz., colour and appearance, flavour, juiciness, texture, and overall acceptability of chicken nuggets enrobed with Black beanflour at two different concentrations in the batter mix, viz., 25% w/w (Batter mix-I) and 35% w/w (Batter mix-II). Enrobing of nuggets with Black bean flour significantly (p<0.05) increased the coating thickness, cooking yield, crude protein, ether extract, ash and crude fiber content of nuggets as the level of flour increased from 25% to 35% in the batter mix, whereas, pH and moisture protein ratio decreased significantly (p<0.05). Enrobing of nuggets with batter containing 25% level of black bean flour resulted in higher scores for almost all the sensory attributes viz., colour and appearance, flavour, texture, juiciness and overall acceptability. Enrobing of nuggets with two different levels of black bean flour revealed a significant (p<0.05) effect on the sensory scores; colour and appearance, flavour, texture and overall acceptability and a non-significant effect on juiciness. Enrobing of nuggets with black bean flour at 25% (w/w) concentration used in the batter mix was found optimum and had better efficacy in terms of improving some physico-chemical characteristics and sensory 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) | 0.001 | 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".