Functionality and consumer acceptability of low‐fat breakfast sausages processed with non‐meat ingredients of pulse derivatives
Bibliographic record
Abstract
BACKGROUND: Owing to recent changes in consumer eating behaviours as well as potential cost savings for processors, pulse ingredients are finding more application in the meat processing industry. In this study, pea ingredients (pea fibre, FB; pea starch, ST; pea flour, PF) and chickpea flour (CF) were used, at 4% addition level, as fat replacers in low-fat breakfast sausages. The impact of these substitutions on processing and sensory characteristics of breakfast sausage was evaluated. RESULTS: While reduction in fat content in breakfast sausage resulted in some detrimental changes in cooking as well as textural characteristics of the product, addition of binders significantly improved these attributes in low-fat breakfast sausages. Overall, treatment formulation did not significantly affect the pH and the instrumental colour attributes of cooked breakfast sausage. Addition of all binders resulted in reduced cooking loss and increased moisture retention in low-fat breakfast sausage (P < 0.05), whereas, FB and ST were significantly more effective in improving water holding capacity of low-fat breakfast sausage. Furthermore, while both FB and ST increased the hardness, cohesiveness and chewiness parameters of low-fat breakfast sausage (P < 0.05), PF and CF had no impact. Generally, the consumer overall liking and flavour acceptability of FB and ST in low-fat breakfast sausage were significantly higher than those of PF and CF. CONCLUSION: The use of ST or FB as a fat replacer in breakfast sausages offers processors improved cook yield without negatively impacting the important sensory attributes of breakfast sausages. © 2021 Society of Chemical Industry.
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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) | 0.002 | 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".