In Vitro Gastrointestinal Digestion and Colonic Fermentation of a Juçara and Apple Juice Blend: Impacts on Phenolic Bioaccessibility and gut microbiota
Bibliographic record
Abstract
This study evaluated the sensory and functional quality of a mixed beverage composed of juçara pulp powder and apple juice. Three formulations were developed by varying the amount of juçara added to the juice. The effects of in vitro gastrointestinal digestion and colonic fermentation on the bioaccessibility and recovery of phenolic compounds, changes in antioxidant capacity, and the production of short-chain fatty acids (SCFAs) and ammonia ions were evaluated. After digestion, 31.31% of anthocyanins (5.06 mg cyanidin-3-glucoside and 2.95 mg cyanidin-3-rutinoside per 200g) reached the colon. Fermentation for 24 hours significantly enhanced antioxidant capacity—by 54.54% (ABTS) and 355.69% (ORAC), reaching 21.62 and 1,786.64 μmol Trolox·mL-1, respectively—indicating strong in situ antioxidant activity. Colonic fermentation also enhanced short-chain fatty acid production (1.69 mmol·L-1), increased Bifidobacterium spp. counts (0.67 Log cycles), and reduced ammonia levels by 18.83%. The beverage showed good sensory acceptance and purchase intention, highlighting its appeal to consumers. Overall, the combination of juçara and apple juice resulted in a product with nutritional and functional properties, especially due to its antioxidant potential and positive effects on gut microbiota.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".