Influence of culture media, pH and temperature on growth and bacteriocin production of bacteriocinogenic lactic acid bacteria
Bibliographic record
Abstract
There has been continued interest in bacteriocins research from an applied perspective as bacteriocins have potential to be used as natural preservative. Four bacteriocinogenic lactic acid bacteria (LAB) strains of Lactobacillus curvatus (Arla-10), Enterococcus faecium (JFR-1), Lactobacillus paracasei subsp. paracasei (JFR-5) and Streptococcus thermophilus (TSB-8) were previously isolated and identified in our lab. The objective of this study was to determine the optimal growth conditions for both LAB growth and bacteriocins production. In this study, various growth conditions including culture media (MRS and BHI), initial pH of culture media (4.5, 5.5, 6.2, 7.4 and 8.5), and incubation temperatures (20, 37 and 44 °C) were investigated for LAB growth measured as optical density (OD), bacteriocin activity determined as arbitrary unit and viability of LAB expressed as log CFU ml −1 . Growth curves of the bacteriocinogenic LAB were generated using a Bioscreen C. Our results indicated that Arla-10, JFR-1, and JFR-5 strains grew well on both MRS and BHI media at growth temperature tested whereas TSB-8 strain, unable to grow at 20 °C. LAB growth was significantly affected by the initial pH of culture media ( p < 0.001) and the optimal pH was found ranging from 6.2 to 8.5. Bacteriocin activity was significantly different in MRS versus BHI ( p < 0.001), and the optimal condition for LAB to produce bacteriocins was determined in MRS broth, pH 6.2 at 37 °C. This study provides useful information on potential application of bacteriocinogenic LAB in food fermentation processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".