Antibacterial efficacy of organic acids recovered from cranberry juice deacidification against Escherichia coli and their application for fresh-cut lettuce preservation within a circular economy strategy
Bibliographic record
Abstract
Minimally processed leafy vegetables, such as fresh-cut lettuce, still pose high food safety risks and are important vehicles of pathogenic Escherichia coli . The limited efficacy of sodium hypochlorite (SH), the most widely used sanitizer, and its likely harmful byproducts led to the search for safer and natural alternatives. This study explored the disinfection potential of organic acids recovery solutions (OARs), mainly composed of citric and malic acids, coproduced during cranberry juice deacidification by bipolar membrane electrodialysis. These OARs were evaluated against E. coli (ATCC 11229) using in vitro methods and by subjecting inoculated (∼ 6 log CFU/g) fresh-cut romaine lettuce leaves to the different OARs, water or 200 ppm SH for a soaking duration of 1 or 4 min. In vitro results demonstrated that the E. coli strain inhibition strongly correlated with organic acids concentration, above a minimal inhibitory concentration (MIC) set around 6.3 mg/mL, and bactericidal effects were outlined. On fresh-cut romaine lettuce, the OARs achieved higher reductions (0.9 – 1.1 log) than water (0.4 log) or SH (0.8 log) within shortest contact time (1 min). Treatment duration had a small but non-significant additional impact, and an extended inhibitory effect was observed over 7 days of storage at 4°C. Regardless of the type of treatment, the quality parameters were unchanged (texture, weight loss, color). For the first time, the potential of OARs from cranberry juice deacidification as a natural substitute to chemical sanitizers was showcased, ensuring fresh-cut produce safety and quality while supporting circular economy principles.
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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".