The use of protein binders and sorghum crisps as potential ingredients in a cereal bar for dogs
Bibliographic record
Abstract
Abstract This study aimed to evaluate the inclusion of different protein binders and sorghum crisps in cereal bars for dogs and their effect on sensory properties, product texture, and dog preference. Fifteen cereal bars were developed in which three crisp sources (rice crisp, white and red sorghum crisp) and five sources of binders (corn syrup, spray dried plasma, gelatin, albumin, and egg product) were evaluated. An interaction effect between binder and crisp sources was found for textural properties (p <.05). A total of 103 volatile compounds were identified and semi‐quantified in the cereal bar samples, with aldehydes being the most represented. Unlike crisp source, protein binders played a major role on sensory properties and impacted the dog's preference. This study suggests that sorghum crisps and protein binders may be used in cereal bars for dogs; however, considerations regarding sensory attributes and dog's preference should be taken to maximize product acceptance. Practical applications This is the first study to report information regarding the use of novel ingredients in a cereal bar application for dogs. The findings observed wherein provide a comprehensive understanding about product development and the impact of ingredients on final product quality, sensory properties, and animal preference. The methodologies and outcomes of our work can be directly translated to the pet food industry to aid in the development of dog treats.
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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 | 0.001 | 0.001 |
| 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".