Effect of Salicylic Acid on Carotenoids and Chlorophyll Content in Mas Cotek (Ficus deltoidea Jack var. trengganuensis) Leaves and its Retinol Activity Equivalents (RAE)
Bibliographic record
Abstract
This study was conducted to determine the effect of different concentrations of salicylic acid (SA) (control, 0.01, 0.10, 1.00 mM) on carotenoids and chlorophyll content and its retinol activity equivalents (RAE) value in Ficus deltoidea Jack var. trengganuensis leaves. In this study, 12 seedlings of Ficus deltoidea Jack var. trengganuensis were sprayed with different concentrations of SA. Carotenoid content was determined using High-Performance Liquid Chromatography (HPLC) and chlorophyll content was determined using chlorophyll meter. Retinol activity equivalents were calculated using RAE formulation. From the results obtained, two types of carotenoids, lutein and β-carotene, were detected in HPLC. The highest lutein and β-carotene content present in Ficus deltoidea Jack var. trengganuensis leaves extract was found in 0.10 mM SA treatment (93.50b ± 0.71 µg/g DW) for lutein and (282.00b ± 46.67 µg/g DW) for β-carotene, while the lowest lutein compound was found in 1.00 mM SA treatment (30.25a ± 1.77 µg/g DW) while the least β-carotenoid content was found in (1.00 mM SA) treatment (63.00a ± 0.71 µg/g DW). The same goes for retinol activity equivalents, the highest retinol activity equivalents was recorded in 0.10 mM SA treatment while lowest in 1.00 mM SA treatment. For chlorophyll content, the highest reading was showed in 0.10 mM SA treatment (73.50b ± 0.71 µg/g DW) while the lowest reading was in 1.00mM SA treatment (42.10a ± 1.41 µg/g DW). From this study, it can be concluded that salicylic acid at a certain concentration could increase or improve the carotenoid or chlorophyll content. Thus, it could be an alternative source of carotenoid and chlorophyll for the food and pharmaceutical industry in the future.
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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 | 0.000 |
| 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) | 0.000 | 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".