Prebiotic Potential of Pulp and By-products from Native Fruits of the Brazilian Savannah
Bibliographic record
Abstract
Native fruits from the Brazilian Savannah, such as jatobá-do-Cerrado (Hymenaea stigonocarpa) and jenipapo (Genipa americana L.), have interesting sensory and nutritional qualities. Jatobá-do-Cerrado consumption is limited due to its aroma and texture. Jenipapo is widely used in food and cosmetics, though its by-products (peel and seeds) are often discarded. The present study evaluates the nutritional aspects (proximate composition, antioxidant capacity, phenolic compounds, and prebiotic potential) as well as the technological properties (water and oil holding capacity) of the jatobá-do-Cerrado pulp (JAP) and the jenipapo by-product (JEBP). JAP and JEBP exhibited high dietary fiber content, mainly insoluble fiber, as well as a high concentration of phenolic compounds and antioxidant activity superior to other Cerrado fruits. JAP and JEBP showed WHC of 3.16 and 4.06 g/g and OHC of 3.09 and 1.14 g/g, respectively, indicating their potential for food processing and product development. Fermentation tests showed that JAP and JEBP supported probiotic (Lactobacillus and Bifidobacterium) growth similarly to fructooligosaccharides (FOS), lowering the medium’s pH. Fermentation also stimulated the synthesis of bioactive amines such as spermidine and phenylethylamine. Thus, they are promising prebiotic ingredients for functional foods and supplements.
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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 | 0.001 | 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) | 0.001 | 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".