Antimicrobial activity of bacteriocin-producing lactic acid bacteria isolated from cheeses and yogurts
Bibliographic record
Abstract
The biopreservation of foods using bacteriocinogenic lactic acid bacteria (LAB) isolated directly from foods is an innovative approach. The objectives of this study were to isolate and identify bacteriocinogenic LAB from various cheeses and yogurts and evaluate their antimicrobial effects on selected spoilage and pathogenic microorganisms in vitro as well as on a food commodity.LAB were isolated using MRS and M17 media. The agar diffusion bioassay was used to screen for bacteriocin or bacteriocin-like substances (BLS) producing LAB using Lactobacillus sakei and Listeria innocua as indicator organisms. Out of 138 LAB isolates, 28 were found to inhibit these bacteria and were identified as strains of Enterococcus faecium, Streptococcus thermophilus, Lactobacillus casei and Lactobacillus sakei subsp. sakei using 16S rRNA gene sequencing. Eight isolates were tested for antimicrobial activity at 5°C and 20°C against L. innocua, Escherichia coli, Bacillus cereus, Pseudomonas fluorescens, Erwinia carotovora, and Leuconostoc mesenteroides subsp. mesenteroides using the agar diffusion bioassay, and also against Penicillium expansum, Botrytis cinerea and Monilinia frucitcola using the microdilution plate method. The effect of selected LAB strains on L. innocua inoculated onto fresh-cut onions was also investigated.Twenty percent of our isolates produced BLS inhibiting the growth of L. innocua and/or Lact. sakei. Organic acids and/or H2O2 produced by LAB and not the BLS had strong antimicrobial effects on all microorganisms tested with the exception of E. coli. Ent. faecium, Strep. thermophilus and Lact. casei effectively inhibited the growth of natural microflora and L. innocua inoculated onto fresh-cut onions. Bacteriocinogenic LAB present in cheeses and yogurts may have potential to be used as biopreservatives in foods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".