Influence of growth parameters on bacteriocin-like inhibitory substances (BLIS) production by lactic acid bacteria
Bibliographic record
Abstract
This study aimed to investigate the possible production of bacteriocin-like inhibitory substances (BLIS) and optimize it by Lactobacillus sakei and combined Lactobacillus curvatus/Pediococcus acidilactici, lactic acid bacteria used for the meat fermentation. The effect of growth parameters on BLIS production was evaluated by measuring the antibacterial activity of the cell-free supernatant of these bacteria. The stability of the cell-free supernatant of combined L.curvatus/P.acidilactici bacteria was also examined after heat treatment and pH variations. Results showed highest BLIS production by L.curvatus/P.acidilactici was at growth temperatures of 28°C and pH 5, while the optimum condition for production of BLIS by L.sakei was 24°C and pH 6.5. The BLIS production by L. curvatus/P. acidilactici bacteria was greatly influenced by carbon and nitrogen sources. The antimicrobial activity of BLIS in supplemented De Man, Rosa, and Sharpe broth (MRS) was 2044.44 AU/mL, 4.84-fold greater than unsupplemented MRS. None of the carbon sources used in this study affected the BLIS production by the L.sakei, while pea peptone enhanced the antimicrobial activity and showed a value of 188.89 AU/mL, 1.47-fold greater than unsupplemented MRS. The characterization of the cell-free supernatant of L. curvatus/P. acidilactici bacteria showed the heat stability and activity of BLIS at pH 2-10.
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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 | 0.001 | 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".