Biofilm formation by heat-resistant dairy bacteria: multispecies biofilm model under static and dynamic conditions
Bibliographic record
Abstract
ABSTRACT In the food industry, especially dairy, biofilms can be formed by heat-resistant spoilage and pathogenic bacteria from the farm. Such biofilms may persist throughout the processing chain and contaminate milk and dairy products continuously, increasing equipment cleaning, maintenance costs, and product recalls. Most biofilms are multispecies, yet most studies focus on single-species models. A multispecies model of dairy biofilm was developed under static and dynamic conditions using heat-resistant Bacillus licheniformis, Pseudomonas aeruginosa, Clostridium tyrobutyricum, Enterococcus faecalis, Streptococcus thermophilus, and Rothia kristinae isolated from dairies. C. tyrobutiricum and R. kristinae were weak producers of biofilm, whereas the other four were moderate to strong producers. Based on cross-streaking on agar, P. aeruginosa was found to inhibit B. licheniformis and E. faecalis . In multispecies biofilm formed on stainless steel in a CDC reactor fed microfiltered milk, the strong biofilm producers were dominant while the weak producers were barely detectable. All biofilm matrices were dispersed easily by proteinase K treatment but were less sensitive to DNase or carbohydrases. Further studies are needed to deepen our understanding of multispecies biofilms and interactions within to develop improved preventive strategies to control the proliferation of spoilage and pathogenic bacteria in dairies and other food processing environments. IMPORTANCE A model of multispecies biofilm was created to study biofilm formation by heat-resistant bacteria in the dairy industry. The biofilm formation potential was evaluated under static conditions. A continuous flow version was then developed to study multispecies biofilm formed on stainless steel in microfiltered milk under dynamic conditions encountered in dairy processing equipment. The study of biofilm composition and bacterial interactions therein will lead to more effective means of suppressing bacterial growth on food processing equipment and contamination of products with spoilage and pathogenic bacteria, which represent considerable economic loss.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".