Study of the ability to form biofilms of microorganisms isolated from the milk industry in Canada
Bibliographic record
Abstract
The ability of microorganisms to form biofilms has become a major problem in the dairy industry in Canada, notably by affecting the quality and the safety of the by-products. Established biofilms are difficult to remove during the CIP cleaning system and may become resistant to sanitizers. Therefore, it is important to identify and characterize the microorganisms associated to biofilm in the Canadian dairy industry, allowing to develop improvement strategies of biofilm control. The purpose of this study is to evaluate the ability to form biofilms by spoilage microorganisms isolated in processing plants in Canada. For this purpose, 19 strains were isolated from problems associated with the formation of biofilms in the dairy industry and identified using a MALDI-TOF mass spectrometer. The single species biofilm production of these isolates was then measured after a crystal violet coloration using 96-well microplates. The results revealed different biofilm formation profiles depending of the isolates in culture medium. Indeed, 7/19 isolates are moderate or strong biofilm producers and 12/19 isolates are negative or weak biofilm producers. Furthermore, enzymatic treatments revealed that the composition of the biofilms was different depending of the species but also the isolates. In conclusion, the results suggest that some of the isolates collected in the dairy industry have the ability to produce moderate or strong biofilms and thus, to facilitate the persistence of other spoilage microorganisms but also potential pathogenic microorganisms such as Listeria monocytogenes. The characterization of those biofilms will be helpful to the development of an effective approach allowing a better control of the biofilms in the dairy industry.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".