Application of Pickering emulsions in probiotic encapsulation- A review
Bibliographic record
Abstract
Probiotics are live microorganisms that confer health benefits to host organisms when consumed in adequate amounts and are often incorporated into foods for human consumption. However, this has negative implications on their viability as large numbers of these beneficial bacteria are deactivated when subjected to harsh conditions during processing, storage, and passage through the gastrointestinal tract. To address these issues, numerous studies on encapsulation techniques to protect probiotics have been conducted. This review focuses on emulsion technology for probiotic encapsulation, with a special focus on Pickering emulsions. Pickering emulsions are stabilized by solid particles, which adsorb strongly onto the liquid-liquid interfaces to prevent aggregation. Pickering emulsions have demonstrated enhanced stability, high encapsulation efficiency, and cost-effectiveness compared to other encapsulation techniques. Additionally, Pickering emulsions are regarded as safe and biocompatible and utilize natural materials, such as cellulose and chitosan derived from plants, shellfish, and fungi, which may also be viewed as more acceptable in food systems than common synthetic and natural molecular surfactants. This article reviews the current status of Pickering emulsion use for probiotic delivery and explores the potential of this technique for application in other fields, such as livestock farming, pet food, and aquaculture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".