A Study on the Screening for Lactic Acid Bacteria from Fura Da Nono with Antibacterial and Bio Preservative Properties
Bibliographic record
Abstract
The study explores the isolation and screening of lactic acid bacteria (LAB) from the traditional Nigerian fermented beverage fura da nono with a focus on their antibacterial and biopreservative properties. A total of fifty LAB strains were isolated using selective MRS agar, and presumptive identification was achieved via phenotypic characterisation. Four isolates displaying prominent antibacterial activity were further identified through 16S rRNA gene sequencing, revealing Lactobacillus plantarum FJ390111, Lactobacillus casei CP14326, and Enterococcus lactis NR117562, with an additional isolate, Lysinibacillus fusiformis, identified as an environmental contaminant. Crude bacteriocin extracts, obtained via ammonium sulfate precipitation and dialysis, were evaluated against foodborne pathogens, including Staphylococcus aureus, Salmonella typhi, and Escherichia coli using agar diffusion assays. The LAB bacteriocins demonstrated optimal inhibitory activity at pH 6 and 35 °C, whereas significant declines in activity were noted at temperatures above 40 °C and during extended storage (96 hours at 37 °C). Graphical data and tables confirm that the bacteriocin extracts produced zones of inhibition ranging from approximately 11 to 15 mm under optimal conditions, with Enterococcus lactis showing particularly moderate to high activity. These results underscore the potential application of LAB-derived bacteriocins as natural bio-preservatives in the food industry. However, the heat sensitivity and degradation over time highlight the need for further formulation improvements. The study concludes that with enhanced stabilisation and rigorous quality control, bacteriocins from traditional LAB can serve as effective alternatives to chemical preservatives in ensuring food safety and extending product shelf life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".