Optimization of an ultrasound‐assisted extraction method to obtain gallic acid‐rich extracts from mango seed kernels
Bibliographic record
Abstract
Gallic acid is a widely recognized bioactive compound that falls under the category of secondary polyphenolic metabolites and is fairly found in mango fruit waste, specifically in mango seed kernel (MSK). This study aimed to adopt a green extraction approach to extract this valuable compound via ultrasound-assisted extraction (UAE) without using organic solvents but only water to obtain hazard-free extracts, and the cost of extraction can be minimal. pH (2-8), solvent ratio (20-60 mL/g), temperature (30-60°C) and time (30-60 min) of extraction were the independent variables used for extraction optimization. Single-factor experiments to obtain working ranges for selected extraction variables were carried out. A central composite design using response surface methodology was used to determine the optimum condition to obtain the maximum yield of gallic acid from MSK. The optimized extraction conditions were 3.9 pH, 36.25 mL/g solvent ratio, and 39.4°C of extraction temperature for 21.3 min. As a result, the optimized yield was 5.76 ± 0.41 mg/g, which was comparably equal to and/or better than the other solvent extraction systems. The results showed that gallic acid could efficiently be extracted via UAE under these optimal conditions. It is safer than extraction systems involving hazardous solvents that can be feasibly used for its nutraceutical and therapeutic applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".