Control of pathogens in fresh pork sausage by inclusion of <i>Lactobacillus sakei</i> BAS0117
Bibliographic record
Abstract
A pork sausage was produced with low sodium content (1.64%) to which Lactobacillus sakei was added with the aim of developing a meat pork sausage for cooking and having technological, organoleptic, and hygienic advantages. The lactic acid bacteria (LAB) L. sakei, Lactococcus sp., and Pediococcus pentosaceus were submitted to extreme pH, temperature, and NaCl conditions. Lactobacillus sakei was used in pork sausage because of its resistance to different culture conditions and its antimicrobial potential. The food-borne pathogens Listeria monocytogenes Scott A, Enterococcus faecalis, and Staphylococcus aureus were used as indicator microorganisms to evaluate the antimicrobial activity of selected LAB strains. Salmonella enterica serotype Choleraesuis is a common pathogen of pigs. To the raw sausage product containing L. sakei and nonpathogenic endogenous microbiota, we added about >104 and <105 CFU/g of S. enterica serotype Choleraesuis to evaluate the inhibitory potential of L. sakei towards this pathogen. Salmonella Choleraesuis was inhibited in the presence of L. sakei over 7 days of storage of the meat product (about 3.0 log cycles reduction). Lactobacillus sakei significantly increased inhibition when compared with the nonfermented sausage. Thus, L. sakei BAS0117 played an important role as an additional hurdle in the fermented meat pork sausage during storage.
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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".