Vitamin C and Phenolic Antioxidants of Jua (Ziziphus joazeiro M.) Pulp: A Rich Underexplored Brazilian Source of Ellagic Acid Recovered by Aqueous Ultrasound-Assisted Extraction
Bibliographic record
Abstract
in Portuguese) is an underexplored fruit from Brazil's northeast. This fruit is rich in antioxidant substances. However, there is a dearth of information about jua's bioactive potential. The present study evaluated two extraction methods (continuous agitation and ultrasound-assisted extraction-UAE) and employed three different solvents (water, ethanol, and acetone) to efficiently recover soluble phenolic compounds. Aqueous extracts obtained by UAE showed the highest total phenolic content (TPC) and antiradical activity. Besides being an eco-friendly procedure, extraction and/or solubility in an aqueous medium is also important for food application. Ellagic acids were the predominant phenolics (80%) found in aqueous jua pulp extract obtained by UAE, as determined by HPLC, while its TPC was 405.8 gallic acid equivalent per gram of fruit. This extract also exhibited a higher scavenging activity towards peroxyl radicals when compared to that of several other fruits from the literature, including grape, strawberry, cranberry, and walnuts, which are known references in terms of antioxidants. This is the first report that demonstrates jua pulp's potential as an alternative source of ellagic acid and other phenolic acids and flavonoids. Therefore, the outcome of this study provides new information that can be useful for functional food and nutraceutical industries.
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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".