Microencapsulation of probiotic lactobacilli with shellac as moisture barrier and to allow controlled release
Bibliographic record
Abstract
BACKGROUND: Rapid dissolution in digestive tract and moisture sorption during ambient storage are the two challenges of dry probiotic preparations. To solve these problems, microcapsules with shellac (LAC) addition containing Limosilactobacillus reuteri TMW 1.656 were designed in this work to provide a good moisture barrier and to provide controlled release in digestive tract, based on the hydrophobicity and acid-resistance of LAC. Four microcapsules were prepared using the method of emulsification/external gelation based on the crosslinking reaction between alginate or LAC with calcium ion, including alginate/sucrose (ALG), alginate/shellac/sucrose (ALG/LAC), alginate/whey protein isolate/sucrose (ALG/WPI) and alginate/whey protein isolate/shellac/sucrose (ALG/WPI/LAC). RESULTS: Measurements of physical properties showed that microcapsules with LAC addition (ALG/WPI/LAC and ALG/LAC) had larger particle size, much denser structure, lower hygroscopicity and slower solubilization in water, which agreed with the primary microcapsule design. Probiotic survivals in digestive juices followed the order of ALG/WPI/LAC ≥ ALG/WPI ≥ ALG/LAC > ALG. Probiotic stability after heating and ambient storage both exhibited the order of ALG/WPI/LAC > ALG/LAC ≈ ALG/WPI > ALG, which can be explained by the decreased hygroscopicity with adding LAC. CONCLUSION: LAC addition contributed to better probiotic survivals after freeze drying, simulated digestion, heating and ambient storage, and whey protein isolate (WPI) addition had a synergistic effect. Microcapsule hygroscopicity was closely related with probiotic survivals after heating and ambient storage, while microcapsule solubilization was closely related with probiotic survivals in simulated juices. Within our knowledge, this is the first report to improve probiotic stability during ambient storage based on LAC hydrophobicity. © 2020 Society of Chemical Industry.
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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".