Optimization of gallic acid-enriched ultrasonic-assisted extraction from mango peels
Bibliographic record
Abstract
Abstract Gallic acid is recognized as a notable bioactive compound among secondary polyphenolic metabolites. In the current study, gallic acid-enriched extracts were obtained from mango peels using different solvents (ethanol or water) via ultrasound-assisted extraction, and optimized yields were compared with the conventional extraction technique (decoction). Independent variables for the optimization through response surface methodology were ethanol concentration (0–60%), solvent ratio (10–50 mL/g), temperature (30–60℃), and time (10–30 min) for ethanolic extraction. However, extraction carried out by using water had extraction conditions of pH (2–8), solvent ratio (20–0 mL/g), extraction temperature (40–70℃), and time (30–60 min). The optimized yield of gallic acid obtained through ethanol was 5.75 ± 0.21 mg/g, whereas 3.14 ± 0.24 mg/g of gallic acid was quantified in extraction through water. The results were compared with the aforementioned conventional method of decoction, and it was concluded that the ethanolic extracts of mango peels showed the highest gallic acid yield and total flavonoid contents. The obtained extracts could be a potential source of polyphenolics, especially gallic acid, for use in nutraceuticals as well as in other food applications.
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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 | 0.000 | 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".