Quantitative PCR reveals the frequency and distribution of 3 indigenous yeast species across a range of specialty cheeses
Bibliographic record
Abstract
Indigenous microorganisms are important components of the complex ecosystem of many dairy foods including cheeses, and they are potential contributors to the development of a specific cheese's sensory properties. Among these indigenous microorganisms are the yeasts Cyberlindnera jadinii, Pichia kudriavzevii, and Kazachstania servazzii, which were previously detected using traditional microbiological methods in both raw milk and some artisanal specialty cheeses produced in the province of Qubec, Canada. However, their levels across different cheese varieties are unknown. A highly specific and sensitive real-time quantitative PCR assay was developed to quantitate these yeast species in a variety of specialty cheeses (bloomy-rind, washed-rind, and natural-rind cheeses from raw, thermized, and pasteurized milks). The specificity of the quantitative PCR assay was validated, and it showed no cross-amplification with 11 other fungal microorganisms usually found in bloomy-rind and washed-rind cheeses. Cyberlindnera jadinii and P. kudriavzevii were found in the majority of the cheeses analyzed (25 of 29 and 24 of 29 cheeses, respectively) in concentrations up to 10 4 to 10 8 gene copies/g in the cheese cores, which are considered oxygen-poor environments, and 10 1 to 10 4 gene copies/ cm 2 in the rind. However, their high abundance was not observed in the same samples. Whereas C. jadinii was present and dominant in all core and rind samples, P. kudriavzevii was mostly present in cheese cores. In contrast, K. servazzii was present in the rinds of only 2 cheeses, in concentrations ranging from 10 1 to 10 3 gene copies/cm 2 , and in 1 cheese core at 10 5 gene copies/g. Thus, in the ecosystems of specialty cheeses, indigenous yeasts are highly frequent but variable, with certain species selectively present in specific varieties. These results shed light on some indigenous yeasts that establish during the ripening of specialty cheeses.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".