Optimization of Gallic Acid-Rich Extract from Mango (Mangifera indica) Seed Kernels through Ultrasound-Assisted Extraction
Bibliographic record
Abstract
Different types of agro-waste provide potential substrates for the extraction of bioactive compounds. Mango waste (e.g., peels and seeds) is one such example and may serve as a source of gallic acid, a well-known bioactive compound classified as a secondary polyphenolic metabolite. Here, we explored the efficacy of ultrasound-assisted extraction (UAE) in extracting gallic acid from mango seed kernels using different solvent concentrations (0–60%), solvent-to-sample ratios (10–50 mL/g), temperatures (30–60 °C), and times (10–30 min). The maximum yield of gallic acid (6.1 ± 0.09 mg/g) was obtained when using a 19.4% solvent concentration, a 29.32 mL/g solvent-to-sample ratio, and the extraction was conducted at 38.47 °C for 21.4 min, similar to the values predicted by the model equation. As compared to the conventional extraction procedure, the extract obtained by the optimized method was found to be significantly different in total phenolic content, total flavonoid content, and radical scavenging activity. Non-significant differences were observed in antimicrobial activity. The results indicate that mango seed kernels may be a good source of phenolics, and those phenolics can be effectively obtained through an optimized UAE method. Hence, mango seed kernels may be utilized as a suitable source of extracting phenolics in nutraceutical and 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".