Enzymatically Treated Spent Cellulose Sausage Casings as an Ingredient in Beef Emulsion Systems
Bibliographic record
Abstract
The objective of this research was to incorporate an ingredient obtained from spent cellulose casings in beef emulsion modeling systems. The test ingredient (residual sausage casing, RSC) was procured from cellulose sausage casings following thermal processing of the sausages. The casings were cleaned of contaminants before a combination of enzymatic hydrolysis and high-speed homogenization was conducted in an effort to improve the functional attributes of the cellulose casing residue (i.e. recycling/upcycling of the spent casings). The beef emulsion modeling systems used in this study consisted of 57.30% beef, 20% water, 15% olive oil, 6% of the combination of RSC and an all-purpose binder, 1.45% NaCl, 0.40% sodium tri-polyphosphate, 0.15% sodium nitrite cure, and 0.0035% sodium erythorbate. The overlying goal here was to test the ability of the RSC ingredient for partial or full replacement of binder ingredients in a beef emulsion system. Therefore, the beef emulsion model systems were prepared with five different levels of the RSC ingredient as a substitution to an all-purpose binder ingredient (0% RSC, 25% RSC, 50% RSC, 75% RSC, and 100% RSC). This study was independently replicated in its entirety three times in a completely randomized design and data were analyzed using a generalized linear mixed statistical model. Emulsion samples were tested for proximate composition, cooking loss, emulsion stability, texture profile analysis, and instrumental color. Overall, technological properties and emulsion stability were lost as the level of the RSC ingredient increased, but low inclusion levels of the RSC ingredient (25% RSC) may help maintain acceptable levels of yield and emulsion stability, while improving the sustainability of the sausage production system.
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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) | 0.000 | 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 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".