Combined effect of beet powder and lentil flour as a partial nitrite substitute on physicochemical, texture and sensory characteristics, color, and oxidative stability of pork bologna
Bibliographic record
Abstract
The combined effect of beet powder (BP; 0.1%, 0.3%, and 0.5%) and 6% lentil flour (LF) as a partial nitrite substitute on quality attributes of pork bologna incorporated with 7.5% mechanically separated pork during 12 weeks of cold storage was evaluated. A randomized block experimental design with nine treatments and five storage times (0, 3, 6, 9, and 12 weeks) was used for pH, thiobarbituric acid reactive substance, protein oxidation (carbonyl and sulfhydryl), and color parameters. Bologna preparation at a pilot plant scale was replicated three times. LF addition resulted (p < 0.05) in higher viscosity, emulsion stability, and lower expressible drip. Together BP and LF decreased protein oxidation during storage but were not as effective as nitrite with respect to lipid oxidation. Bologna with BP addition showed lower lightness and higher redness, whereas LF increased lightness and decreased redness. A consumer panel rated color acceptability of bologna lower with LF addition, whereas BP raised color acceptability. However, addition of the highest BP level alone to the bologna was negatively perceived as a result of the low acceptability of purge color of the vacuum-packaged bologna slices. Consequently, the combination of BP and LF could be used to improve eating quality and stability attributes when used as a potential nitrite substitute.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".