Customizing Sanitization Protocols for Food-Borne Pathogens Based on Biofilm Formation, Surfaces and Disinfectants—Their Two- and Three-Way Interactions
Bibliographic record
Abstract
Food-borne pathogens are a serious challenge in food handling, processing, and packaging systems. The growth of microbial biofilms on food handling surfaces further complicates the management of the microbial contamination of food. Microorganisms within biofilms are difficult to eradicate with chemical disinfectants, with an increased likelihood of survival and the subsequent contamination of food. Therefore, a biofilm approach is needed in food safety and hygiene studies. Since many factors, such as strain, cell density, surface type and texture, environmental stress, and so forth, can affect biofilm formation and disinfectant efficacy, we evaluated the responses of biofilms formed by three food-borne bacterial pathogens on eight hard surfaces to seven chemical disinfectants. The three bacteria showed different capacities to colonize the surfaces. Similarly, chemical disinfectants also varied in efficacy, on surfaces and with pathogen species. One-, two-, and three-way interactions of strain, surface, and disinfectant were observed. The results generated demonstrate that the fine-tuning of sanitization strategies along the food production, processing, and packaging chain can be achieved in specific scenarios by accounting for two- and three-way interactions among bacteria, surface, and disinfectant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".