Profiling of phenolic compounds, antimicrobial, antioxidant, and hemolytic activity of mango seed kernel using different optimized extraction systems
Bibliographic record
Abstract
Mango seed kernels (MSKs) have been reported to show antioxidant, antibacterial, and anti-inflammatory properties. This study explores the influence of different optimized extraction systems on the extraction of MSK. The effects on gallic acid (GA) content, total phenolic content (TPC), total flavonoid content (TFC), antioxidant, antimicrobial, and hemolytic activity of MSK extracts from different extraction systems (65.45% ethanol-ultrasound assisted extraction [UAE], 62% ethanol-incubator shaker, 19.4% ethanol-UAE, and 100% water-UAE) were assessed. Based on the results, a nonsignificant difference in phenolic (p = 0.222), flavonoids (p = 0.058), antioxidant (p = 0.165), and antimicrobial activity (p = 0.193) against Staphylococcus aureus whereas a significant difference (p < 0.0001) in hemolytic, GA content, and antimicrobial activity against Clostridium perfringens was observed. Among different extraction systems, aqueous extraction showed significantly lower hemolytic (1.09%) and higher GA content (4.72 mg/g) and comparable results in all other experiments; yield (32.40%), TPC (58.79 mg/g), TFC (2.16 mg/g), and antioxidant (73.19%). Hence, it has been concluded that aqueous extraction system could be considered a sustainable extraction system for practical applications. PRACTICAL APPLICATION: Aqueous extraction system could be a sustainable option for extraction of mango seed kernel for practical applications as it is readily available, cheap, nonflammable, and nontoxic.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".