Evaluation of poultry protein isolate as a food ingredient: Physicochemical characteristics of low-fat turkey bologna
Bibliographic record
Abstract
Evaluation of poultry protein isolate (PPI) as a food ingredient was carried out by substituting nonmeat ingredients such as soy protein isolate (SPI) or meat protein in turkey bologna. Two concentrations (1.5 and 2% dry weight basis) of PPI prepared from mechanically separated turkey meat were used in this study. Two control samples were prepared with 11 and 13% meat protein, respectively. Physicochemical characteristics of turkey bologna containing PPI were compared with those of control and SPI-containing samples. Batter strength was higher for 2% PPI and 13% meat protein control samples (control-2) compared with all other treatments. Cooking yield of the 11% meat protein control was significantly (P < 0.05) less compared with other treatments. However, there was no significant difference in the expressible moisture or purge loss among all the treatments. Control-2 showed lower L* values and was more reddish during refrigerated storage. Addition of protein isolates caused a significant increase (b* value varied between 11.48 and 12.52) in yellowness of products. Turkey bologna with added protein isolates showed significantly lower lipid oxidation as indicated by induced TBA reactive substance analysis. Results from this study suggest that SPI or meat protein could be replaced by PPI without negatively affecting product characteristics as evident from cooking yield and purge loss values.
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.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".