Microencapsulation of fermented wild blueberry to improve the stability of (poly)phenols
Bibliographic record
Abstract
• Wild blueberry bioactives have poor stability and bioaccessibility. • Microbial fermentation enhanced bioactive constituents of wild blueberries • Controversial maltodextrin partially replaced with inulin in bioactive microencapsulation • Novel microencapsulation enhanced the stability of bioactive constituents • Dual fermentation and microencapsulation generated novel functional food ingredients Dual fermentation augments the diversity and efficacy of wild blueberry bioactives, while microencapsulation ensures their stability and marketability. Here, we have subsequently fermented wild blueberries using Saccharomyces cerevisiae and Komagataeibacter spp. and microencapsulated the end products using different prebiotic fibers and plant proteins as alternatives to controversial maltodextrin. Biotransformation generated health-promoting postbiotics including (poly)phenol metabolites and short-chain fatty acids. The microparticle formulation comprising inulin and maltodextrin (1:1 w/w) exhibited desirable properties similar to conventional microencapsulation products including moisture content (6.07 ± 1.3%), hygroscopicity (7.11 ± 0.4%), particle size (36.9 ± 12 μm), encapsulation efficiency (76.7 ± 3%), and loading capacity (0.348 ± 0.08%). The novel microparticles displayed robust stability under UV light exposure and storage at 4, 20, and 35 °C compared to non-encapsulated fermented wild blueberries. In conclusion, dual fermentation and prebiotic inulin-based microencapsulation pave the way for innovative, safe, and potentially health-enhancing novel food ingredients.
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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".