Comparative assessment of biofilm sampling methods on stainless steel surfaces in a CDC biofilm reactor
Bibliographic record
Abstract
The presence of biofilms on stainless steel surfaces in the dairy industry greatly limits the efficiency of the cleaning procedures. The matrix of extracellular polymeric substances produced by the embedded bacteria is largely responsible for this irreversible binding. Therefore, to detach the biofilm in its entirety from the surface for microbiological identification and physico-chemical characterization is limited with the classical methods commonly used for surface sampling such as swabbing. The objective of this study is to optimize an extraction technique of biofilm formed using a dynamic CDC bioreactor system by a strain of Pseudomonas fluorescens isolated from the dairy industry during a biofilm issue. Three methods: swabbing, scraping and sonic brushing were tested in order to determine which one of these techniques allows a better recovery of the biofilm. They were also compared to sonication which is the standard method established by ASTM International. The results demonstrated that the total viable counts obtained by scraping (8.65 ± 0.07 CFU/cm2) were not significantly different from those achieved by sonication (8.74 ± 0.06 CFU/cm2) in contrast to the other two approaches, while scanning electron microscopy showed an effective removal of biofilms from surfaces by sonic brushing. In conclusion, other combinations including brushing, sonication and/or scraping must be investigated for representative sampling of biofilm on the surfaces of dairy plants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".