Use of Poultry Protein Isolate As a Food Ingredient: Sensory and Color Characteristics of Low‐Fat Turkey Bologna
Bibliographic record
Abstract
UNLABELLED: The potential of using poultry protein isolate (PPI) as a food ingredient to substitute either soy protein isolate (SPI) or meat protein in turkey bologna was investigated. PPI was prepared from mechanically separated turkey meat using pH-shift technology and the prepared PPI was added to turkey bologna at 2 different concentrations (1.5% and 2% dry weight basis). Product characteristics were compared with those prepared with the addition of 2% SPI, 11% meat protein (control-1), or 13% meat protein (control-2). All the 5 treatments were subjected to sensory analysis to evaluate aroma, appearance, color, flavor, saltiness, juiciness, firmness, and overall acceptability of the turkey bologna samples using 9-point hedonic scales. A turkey bologna control sample with 11% meat protein appeared to be softer compared to other treatments as revealed by texture profile analysis while purge loss during storage in a retail display case was significantly (P < 0.05) higher compared to other treatments. Lightness (L*) value of the products decreased during 4 wk of retail storage. A turkey bologna control sample with 13% meat protein appeared to be darker and more reddish compared to other treatments. Replacing meat protein with protein isolates caused increase in yellowish color of turkey bologna. Sensory analysis concluded that 1.5% PPI and 2% PPI could be used as substitute of SPI or lean meat and the treatments could be improved by increasing saltiness and decreasing firmness. PRACTICAL APPLICATION: The study revealed that with slight modifications in saltiness, turkey bologna can be prepared with the addition of poultry protein isolates as an acceptable substitute for soy protein isolate or meat protein. This will help to avoid usage of nonmeat ingredients (as SPI substitute) and to reduce the cost of production (as meat protein substitute) of low-fat turkey bologna.
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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 | 0.001 | 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 | 0.001 |
| 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".