Recent advances in Pickering emulsions for inhibiting foodborne bacteria
Bibliographic record
Abstract
Foodborne bacteria pose significant risks to public health and economic conditions. Natural antibacterial agents have proven effective against foodborne bacterial contamination but often suffer from harsh environmental and processing conditions. Pickering emulsions, recognized for good stability, high loading capacity, sustained release, cost-effectiveness, low toxicity, biocompatibility, and sustainability, have emerged as efficient carriers for natural antibacterial agents and shown great promise in preserving fruits, vegetables, meats, seafood, and other foods by either direct coating or incorporation in active packaging films. With the growing interest in Pickering emulsions loaded with natural antibacterial agents, in this review, we highlight recent advances in their applications for inhibiting foodborne bacteria. The mostly investigated foodborne bacteria and the compositions and antibacterial mechanisms of natural antibacterial agents loaded Pickering emulsions are summarized, and food-related applications are spotlighted. Despite challenges, such as scalability and regulatory approval, antibacterial Pickering emulsions show great potential as sustainable preservatives for food preservation. • The most investigated foodborne bacteria are summarized. • Compositions and mechanisms of natural antibacterial agents are highlighted. • Special attention is paid to food-related applications. • Challenges and opportunities for future development are discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".