The Composition of Camembert Cheese-Ripening Cultures Modulates both Mycelial Growth and Appearance
Bibliographic record
Abstract
The fungal microbiota of bloomy-rind cheeses, such as Camembert, forms a complex ecosystem that has not been well studied, and its monitoring during the ripening period remains a challenge. One limitation of enumerating yeasts and molds on traditional agar media is that hyphae are multicellular structures, and colonies on a petri dish rarely develop from single cells. In addition, fungi tend to rapidly invade agar surfaces, covering small yeast colonies and resulting in an underestimation of their number. In this study, we developed a real-time quantitative PCR (qPCR) method using TaqMan probes to quantify a mixed fungal community containing the most common dairy yeasts and molds: Penicillium camemberti, Geotrichum candidum, Debaryomyces hansenii, and Kluyveromyces lactis on soft-cheese model curds (SCMC). The qPCR method was optimized and validated on pure cultures and used to evaluate the growth dynamics of a ripening culture containing P. camemberti, G. candidum, and K. lactis on the surface of the SCMC during a 31-day ripening period. The results showed that P. camemberti and G. candidum quickly dominated the ecosystem, while K. lactis remained less abundant. When added to this ecosystem, D. hansenii completely inhibited the growth of K. lactis in addition to reducing the growth of the other fungi. This result was confirmed by the decrease in the mycelium biomass on SCMC. This study compares culture-dependent and qPCR methods to successfully quantify complex fungal microbiota on a model curd simulating Camembert-type cheese.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".