Incorporation of Multi-Strain Probiotic Preparation in a Traditional Brazilian Cheese: Effects on Microbiological Safety and Bacterial Community
Bibliographic record
Abstract
Consumer preference for raw milk cheeses has increased in the past few years. This occurred partly due to their more diverse, enjoyable characteristics, but also due to claims that certain members of the autochthonous microbiota of milk can be beneficial to human health. These microorganisms can inhibit the growth of undesirable microorganisms and may also be used to establish a biogeographic identity for these products. The aim of this study was to assess the effect of a multi-strain probiotic preparation on the microbiological safety and composition of bacterial community of a traditional Brazilian raw milk cheese by means of culture dependent and methods and pyrosequencing. Probiotic enriched cheeses presented an average of 50% less sequence reads belonging to Enterobacteriaceae than control cheeses. Total and thermotolerant coliforms cell viability decreased throughout ripening in two seasons (summer and autumn), while in the winter the presence of these microorganisms was negligible since the beginning of ripening. Results obtained through culture dependent method did not correlate with culture independent method, which pointed to a relatively constant number of Enterobacteriaceae reads during ripening. Viable cells of coagulase positive Staphylococcus aureus stayed within legal limits in both groups of cheeses since the first day and decreased to zero at the 15th day in probiotic enriched cheeses. Salmonella sp. and Listeria sp. were absent in both control and probiotic groups. Our results support that enriching raw milk cheeses with probiotic bacteria or other bioprotective bacteria may help mitigate off flavors produced by Enterobacteriaceae and result in safer products by inhibiting the growth of these microorganisms, while maintaining the microbial diversity that may be beneficial to sensory profiles and health-promoting characteristics. We also showed that this traditional cheese, if made under right the conditions, can meet legal parameters in much less than 60 days of ripening.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".