Diversity and dynamics of lactobacilli populations during ripening of RDO Camembert cheese
Bibliographic record
Abstract
The diversity and dynamics of Lactobacillus populations in traditional raw milk Camembert cheese were monitored throughout the manufacturing process in 3 dairies. Culture-dependent analysis was carried out on isolates grown on acidified de Man - Rogosa - Sharpe agar and Lactobacillus anaerobic de Man Rogosa Sharpe agar supplemented with vancomycin and bromocresol green media. The isolates were identified by polymerase chain reaction - temperature gradient gel electrophoresis (PCR-TGGE) and (or) species-specific PCR and (or) sequencing, and Lactobacillus paracasei and Lactobacillus plantarum isolates were characterized by pulsed field gel electrophoresis (PFGE). Milk and cheese were subjected to culture-independent analysis by PCR-TGGE. Presumed lactobacilli were detected by plate counts throughout the ripening process. However, molecular analysis of total DNA and DNA of isolates failed to detect Lactobacillus spp. in certain cases. The dominant species in the 3 dairies was L. paracasei. PFGE analysis revealed 21 different profiles among 39 L. paracasei isolates. Lactobacillus plantarum was the second most isolated species, but it occurred nearly exclusively in one dairy. The other species isolated were Lactobacillus parabuchneri, Lactobacillus fermentum, Lactobacillus acidophilus, Lactobacillus helveticus, a Lactobacillus psittaci/delbrueckii subsp. bulgaricus/gallinarum/crispatus group, Lactobacillus rhamnosus, Lactobacillus delbrueckii subsp. bulgaricus, L. delbrueckii subsp. lactis, Lactobacillus brevis, Lactobacillus kefiri, and Lactobacillus perolens. Lactobacilli diversity at the strain level was high. Dynamics varied among dairies, and each cheese exhibited a specific picture of species and strains.
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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 | 0.001 | 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".