Boosting Antioxidant Activity in Butterfly Pea via Banana Peel-Tithonia Organic Fertilizers
Bibliographic record
Abstract
Butterfly pea (Clitoria ternatea) is a promising source of natural antioxidants due to its high anthocyanin, flavonoid, and phenolic content.This study aimed to optimize its productivity and antioxidant activity through the application of liquid organic fertilizer (LOF) from Kepok banana peel and Mexican sunflower green manure, combined with different planting media on Andisol soil.The research used a completely randomized design with two fertilizer types and four planting media, repeated three times, resulting in 72 experimental units.Conducted at Trawas Experimental Farm and the Soil Laboratory of UPN "Veteran" East Java from April to June 2024, results showed that the combination of soil and compost with Mexican sunflower green manure yielded the highest plant biomass (231.09g).Meanwhile, the combination of soil and raw husk with Kepok banana peel LOF significantly increased antioxidant activity by 24.17%.The highest harvest index (0.29) was also observed in the soil-compost combination.Soil and compost improved water retention and nutrient availability, while raw husk may have triggered stress responses and activated secondary metabolite production.These findings suggest that appropriate organic fertilizer and planting media combinations can enhance both biomass and antioxidant content of butterfly pea flowers.
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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) | 0.000 | 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 | 0.000 |
| 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".