Evaluation of the Efficacy of Organic Peroxyacids for Eradicating Dairy Biofilms Using an Approach Combining Static and Dynamic Methods
Bibliographic record
Abstract
The presence of biofilms in the dairy industry is of major concern, as they may lead to the production of unsafe and altered dairy products due to their high resistance to most clean-in-place (CIP) procedures frequently used in processing plants. Therefore, it is imperative to develop new biofilm control strategies for the dairy industry. This protocol is aimed at evaluating the efficacy of organic peroxyacids (peracetic, perpropionic, and perlactic acids and a commercial peracetic acid-based disinfectant) for eradicating dairy biofilms using a combination of static and dynamic methods. All the disinfectants were tested on the strongest biofilm-producing bacteria in either a single or a mixed biofilm using the minimum biofilm eradication concentration (MBEC) assay, a static high-throughput screening method. A contact time of 5 min with the disinfectants at the recommended concentrations successfully eradicated both the single and mixed biofilms. Studies are currently ongoing to confirm these observations using the Center for Disease Control (CDC) biofilm reactor, a dynamic method to mimic in situ conditions. This type of bioreactor enables the use of a stainless-steel surface, which constitutes most industrial equipment and surfaces. The preliminary results from the reactor appear to confirm the efficacy of organic peroxyacids against biofilms. The combined approach described in this study may be used to develop and test new biological or chemical formulations for controlling biofilms and eradicating microorganisms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".