Assessment of Probiotic Viability during Cheddar Cheese Manufacture and Ripening Using Propidium Monoazide-PCR Quantification
Bibliographic record
Abstract
The use of a suitable food carrier such as cheese could significantly enhance probiotic viability during storage. The main goal of this study was to assess viability of commercial probiotic strains during Cheddar cheesemaking and ripening (4-6 months) by comparing the efficiency of microbiological and molecular approaches. Molecular methods such as quantitative PCR (qPCR) allow bacterial quantification, and DNA-blocking molecules such as propidium monoazide (PMA) select only the living cells' DNA. Cheese samples were manufactured with a lactococci starter and with one of three probiotic strains (Bifidobacterium animalis subsp. lactis BB-12, Lactobacillus rhamnosus RO011, or Lactobacillus helveticus RO052) or a mixed culture containing B. animalis subsp. lactis BB-12 and L. helveticus RO052 (MC1), both lactobacilli strains (MC2), or all three strains (MC3). DNA extractions were then carried out on PMA-treated and non-treated cell pellets in order to assess PMA treatment efficiency, followed by quantification using the 16S rRNA gene, the elongation factor Tu gene (tuf) or the transaldolase gene (tal). Results with intact/dead ratios of bacteria showed that PMA-treated cheese samples had a significantly lower bacterial count than non-treated DNA samples (P < 0.005), confirming that PMA did eliminate dead bacteria from PCR quantification. For both quantification methods, the addition of probiotic strains seemed to accelerate the loss of lactococci viability in comparison to control cheese samples, especially when L. helveticus RO052 was added. Viability of all three probiotic strains was also significantly reduced in mixed culture cheese samples (P < 0.0001), B. animalis subsp. lactis BB-12 being the most sensitive to the presence of other strains. However, all probiotic strains did retain their viability (log 9 cfu/g of cheese) throughout ripening. This study was successful in monitoring living probiotic species in Cheddar cheese samples through PMA-qPCR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".