The use of protein binders and sorghum crisps as potential ingredients in a cereal bar for dogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.000 | 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 it