Effect of high pressure processing combined with lactic acid bacteria on the microbial counts and physicochemical properties of uncooked beef patties during refrigerated storage
Bibliographic record
Abstract
The effect of high pressure processing (HPP) at 300, 400, and 500 MPa combined with Lactobacillus acidophilus was assessed on uncooked ground beef patties (BP) during refrigerated storage of 10 days. BP were evaluated in terms of microbial growth, color, pH, and lipid oxidation during the storage. Untreated BP had a significantly higher total aerobic count (6.74 log CFU/g) than BP treated with HPP 500 MPa + L. acidophilus (3.35 log CFU/g) on Day 10 of storage. Yeasts and mold counts of only 0.80 log CFU/g were detected in BP treated with HPP 500 MPa + L. acidophilus on Day 10 of storage. BP treated with combined process showed significant color retention, a delayed decrease in pH value, and inhibition of lipid oxidation during storage. The HPP 500 MPa + L. acidophilus showed the highest scores for all sensory parameters. This combined process can be used as an effective treatment for maintaining the microbial stability and quality of BP during storage. Novelty impact statement High pressure processing at 500 MPa was applied as a postprocessing treatment in conjunction with lactic acid bacteria for improving the microbiological safety and enhancement of shelf-life of fresh ground beef patties during refrigerated storage for intended usage at home and fast-food restaurants.
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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) | 0.001 | 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".