Effect of Ultrasound-Assisted Extraction Parameters on Total Polyphenols and Its Antioxidant Activity from Mango Residues (Mangifera indica L. var. Manililla)
Bibliographic record
Abstract
Manililla is a mango variety whose residues contain bioactive compounds such as polyphenols and flavonoids, with high added value. The use of environmentally friendly extraction technology would be of great relevance; hence, this study aimed to evaluate the effect of solvent relation, sonication time and amplitude on the ultrasound-assisted extraction of total polyphenols in Manililla mango residues (peel, endocarp and kernel) and antioxidant activity. An experimental design 23 with a central point was used to evaluate the curvature behavior of the process variables. Conventional maceration was used as a control. The better conditions were obtained at the central point using 50% ethanol in water, 60% amplitude and 20 min of sonication time. We obtained values of up to 1814 mg GAE/100 g, 469 mg GAE/100 g and 672 mg GAE/100 g of total polyphenols and 1228 mg QE/100 g, 653 mg QE/100 g and 880 mg QE/100 g of total flavonoids for peel, endocarp and kernel, respectively. Mangiferin was quantified in ultrasound-assisted extraction at 150 mg/g in peel and 0.025 mg/g in the kernel, but it was not detectable in maceration. An antioxidant capacity of 87%, 14% and 83% inhibition for peel, endocarp and kernel, respectively, were obtained. Peel and kernel were the residues with higher potential as extraction material, while endocarp was not.
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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".