Utilization of Faba Bean Protein Emulsion‐Based Films for Reducing Pathogen Survival on Artificially Inoculated Fresh Meat
Bibliographic record
Abstract
ABSTRACT The overall goal of this research was to develop faba bean protein emulsion‐based films with antimicrobial capabilities using oregano essential oil (OEO), nisin, and ethylenediaminetetraacetic acid (EDTA) and to evaluate their efficacy in reducing pathogen survival on artificially inoculated fresh meat (eye‐of‐round steak). Initial screening of film‐forming solutions showed that combining OEO with higher concentrations of nisin and EDTA was most effective in inhibiting Salmonella enteritidis , Escherichia coli , and Staphylococcus aureus . OEO was incorporated into films using either high‐shear homogenization (HSH) [0%, 1%, 2%, and 3%, and 1% with 10 mg nisin and 320 mg EDTA] or high‐pressure homogenization (HPH) [0%, 1%, and 1% with 10 mg nisin and 320 mg EDTA] to examine the effect of oil droplet size on film characteristics and bactericidal efficacy. A week‐long pathogen survival study showed that the viable bacterial load was reduced by 2.5, 4, and 1.9 log for each of the bacteria, respectively, for films produced with HSH, versus 2.2, 2.3, and 1.1 log for those produced with HPH. Nisin and EDTA did not have a significant effect on film characteristics, whereas OEO increased film elasticity (115% for 0% OEO vs. 204% for 3% OEO) and decreased water vapor permeability, but only when HPH was used (0.67 g·mm/kPa·h·m 2 for the 0% OEO films [HSH] vs. 0.63 g·mm/kPa·h·m 2 for the 1% OEO [HSH] vs. 0.48 for the 1% OEO [HPH]). These results suggest that, while HPH can improve certain characteristics of protein‐based films, it also reduced their antimicrobial capacity.
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 | 0.000 | 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 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".