Adhesion of lactic acid bacteria to food contact surfaces, mechanisms, evolutionary rationale and prevention strategies
Bibliographic record
Abstract
The adhesion of three lactic acid bacterial strains to glass, stainless steel and Teflon was investigated, for the ultimate purpose of determining means to prevent their adhesion to these surfaces in food processing environments. In accordance with the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, none of the strains adhered significantly to any of the surfaces when immersed in a distilled water medium, and the most negatively charged strain, ' Lactobacillus plantarum' B212, did not adhere significantly to glass or stainless steel when immersed in phosphate buffered saline. An electrochemical method, novel to bacterial adhesion studies, revealed that all three lactic acid bacterial strains employ two separate attachment mechanisms: one on highly negatively charged surfaces, and the other on less negatively charged surfaces. Mathematical modeling of adhesion relative to time indicated that ' Lactobacillus brevis' may detach from stainless steel surfaces when immersed in PBS buffer. Pre-conditioning stainless steel slides with 2% milk for 15 min was found to inhibit the adhesion of 'L. brevis' and 'L. plantarum' B212, by 99.1% and 100% respectively, relative to unconditioned slides, when these strains were immersed in apple juice over a 4 day period.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".