A comprehensive review on microencapsulation of probiotics: technology, carriers and current trends
Bibliographic record
Abstract
Consumer interest in probiotics is significantly growing due to their positive impact on their health. Dairy products are common and most preferred probiotics "delivery vehicle" in the food industry. However, dairy products are associated with increased risk to people, with lactose intolerance, galactosemia, allergy to milk proteins, and high cholesterol levels. For such cases, non-dairy based probiotic foods could offer a good alternative. Among non-dairy foods, fruit juice is more dietary inclusive, convenient, and well accepted by all the age groups. Therefore, fruit juice could be used as a suitable non-dairy food carrier in probiotic delivery. Lactobacillus and Bifidobacterium are the two main strains used commercially worldwide for preparing probiotic products with proven health benefits. However, protecting the probiotic cells is the key for probiotic formulation in order to guarantee the survival. Therefore, various encapsulation techniques, cell viability, and suitable carrier materials in downstream processing and utilization are discussed in the review. Among different encapsulation techniques, spray drying emerged as an alternative technique for better utilization of probiotics in fruit juices with possibilities for industrial applications due to cost-effective and continuous process. Therefore, spray drying could be considered as an efficient encapsulation technique in food industry for fruit juice probiotification.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".