Methanol, Ethanol, and Acetone Result in Non Different Concentration of Total Phenolic Content in Mangosteen (Gacinia mangostana L.) Peel
Bibliographic record
Abstract
Peel extract of Mangosteen (Gacinia mangostana L.) can controll Aspergillus flavus, and Colletotrichum gloeosporioide since the peel contains high total phenolic content. However, different solvents might result in a different extraction yield of total phenolics. This research studied the effect of 4 solvents, acetone, ethanol, methanol, and water on total phenolic content in the fresh and dry peel of mangosteen. Folin-Ciocalteu method was used for total phenolic content analysis. The result demonstrated that mangosteen peel extracted with methanol, ethanol, and acetone contained non significant difference of total phenolic contents in dry mangosteen peel. Extraction of the peel with water resulted in less total phenolic content than other solvents in both fresh and dry magosteen peel. In conclusion, to obtain high total phenolic content, methanol, ethanol, and acetone, should be used for the extraction of total phenolic content from mangosteen peel.
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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 | 0.001 | 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) | 0.002 | 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".