Biofouling of ultrafiltration membrane by dairy fluids: Characterization of pioneer colonizer bacteria using a DNA metabarcoding approach
Bibliographic record
Abstract
Biofouling of filtration membranes is a major quality and performance issue for the dairy industry. Because biofilms that survive cleaning cycles become resistant over time, prevention strategies limiting the adhesion of bacteria to membranes should be prioritized for sustainable control of biofouling. However, this cannot be achieved because the pioneer bacteria colonizing these membranes are still unknown. Consequently, the objective of this study was to characterize pioneer bacteria on the filtration membrane surface and to measure the effect of filtration operational parameters on their diversity. Thus, milk and cheese whey were filtered for 5 h in concentration mode at 10 and 40°C using a laboratory-scale crossflow filtration system equipped with flat-sheet ultrafiltration membranes. Pioneer colonizer bacteria found on membranes after a chlorinated alkaline cleaning cycle were identified using a metabarcoding approach targeting the 16S ribosomal RNA genes. Our results suggested that prevention strategies targeting biofouling should consider the nature of the filtered fluid and the feed temperature (36.15 and 5.09% of the variances observed on membranes, respectively), as well as the microbial environment of the dairy processing plant. In the future, it is hypothesized that cleaning prevention strategies will be specific to each dairy processor and their operational parameters.
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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) | 0.000 | 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 | 0.000 | 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 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".