Optimized extraction and characterization of antimicrobial phenolic compounds from mangosteen (<i>Garcinia mangostana</i> L.) cultivation and processing waste
Bibliographic record
Abstract
BACKGROUND: Applications for antimicrobials derived from the mangosteen (Garcinia mangostana L.) plant are presently restricted by high production costs. Extraction from cultivation or processing waste streams using a solvent-free approach could lessen to permit commercial applications in food processing and preservation. RESULTS: Phenolics were extracted from mangosteen bark, leaf and fruit pericarp in methanol and in water using response surface methodology to optimize recovery. Initial examination of antimicrobial effects revealed a lack of antimicrobial activity against fungi and weak activity against the Gram-negative bacteria Escherichia coli and Salmonella typhimurium. In contrast, extracts prepared from bark or fruit pericarp exhibited strong pH-dependent bacteriostatic and bactericidal effects against Listeria monocytogenes and Staphylococcus aureus. Activity was slightly weaker in aqueous extracts due to lower concentrations of tartaric acid esters and flavonols than in methanolic extracts. Measurement of propidium iodide uptake and ATP leakage indicated that the extracts induced damage to the membrane of Gram-positive bacteria. CONCLUSION: Extracts of mangosteen bark and fruit pericarp contain mixtures of phenolic compounds with activity against Gram-positive bacteria, notably Listeria monocytogenes. Extraction of phenolics from mangosteen waste could yield fractions for potential applications in the formulation of low-cost processing aids or sanitizers for the food industry.
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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 | 0.000 | 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 | 0.001 |
| 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".