Isolation and Characterisation of Lactic Acid Bacteria Isolated from Artisanal Cheese (Tchoukou) from the Niger Republic
Bibliographic record
Abstract
Lactic Acid Bacteria (LAB) are industrially essential microorganisms widely used as starter cultures in the production of dairy, meat, cereal, vegetable, and alcoholic beverages. Beyond their technological role, LAB contribute to human health by enhancing gastrointestinal function, producing antioxidant metabolites, inhibiting pathogenic bacteria, and supporting immune activity. Isolation and characterisation are essential steps in evaluating their functional properties. In this study, LAB were isolated from artisanal cow milk cheese, known locally as Tchoukou, using selective media. Identification relied on phenotypic, biochemical, and genotypic traits. 16S rDNA analysis of isolates from Maradi, Tahoua, and Zinder regions of Niger revealed clustering with Lactobacillus plantarum (76.48%) and Lactococcus garvieae. Phenotypic and biochemical tests confirmed Lactobacillus as rod-shaped, Gram-positive, and Lactococcus as coccoid, Gram-positive. All isolates were catalase-negative and sugar-fermenting, supporting their taxonomic classification. Functional assays showed tolerance to acidic conditions (pH 3.0–3.5) and optimal growth at 37–40 °C. Antibiotic susceptibility testing indicated sensitivity to gentamicin, sulphamethoxazole, chloramphenicol, ampicillin, tetracycline, and co-trimoxazole. Findings highlight the dominance of Lactobacillus in Tchoukou cheese and suggest probiotic potential due to acid tolerance. However, antibiotic sensitivity patterns warrant further functional evaluation for industrial applications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".