Effects of novel nitrite packaging film on the bacterial growth of bison strip-loin steaks
Bibliographic record
Abstract
This study aimed to determine the effect of different packaging types (overwrapped with polyvinyl chloride film = PVC, vacuum skin packaging = VSP, and vacuum skin packaged with nitrite film = NIT), swabbed steak surface (bottom and top) and ageing time (2 and 9 days) on color stability and spoilage bacteria counts of bison striploin steaks after 7 days of retail display. In comparison to PVC packaging, both VSP and NIT showed more stable color and reduced the bacterial load on the steaks by ∼2log10 for coliforms (p < .0001), ∼0.5 log10 for lactic acid bacteria (p = .005), and ∼1.5 log10 for psychrophilic bacteria (p = .0001). Ageing time (p < .05) increased all spoilage bacteria numbers (∼2 log). The NIT packaging system improved retail color stability of bison steaks; however, the decreased growth of bacteria during retail display was similar to other anaerobic packaging systems. Practical applications Nitrite film packaging (FreshCase®), is a new smart packaging technology which contacts the meat surface, and nitrite embedded in the film surface dissolves into the meat juices, creating a stable, bright-red color in a vacuum package without curing the meat. The FreshCase® technology is already approved for use in the United States providing economic and market advantages to United States retailers. The packaging can offer more than 30 days of shelf life for whole muscle beef and extends typical retail display life up to 28 days compared to 3–5 days for tray over-wraps. This technology opens the possibility to the Bison industry to expand its market, where color stability is an issue and has the potential to eliminate retail losses due to poor color stability and early browning. This will allow the bison industry to transform from mainly frozen, to a more premium fresh meat market.
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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) | 0.000 | 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) | 0.001 | 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".