GCTOF-MS and HPLC Identification of Phenolic Compounds with Different Fractional Extracts of Lepironia articulata
Bibliographic record
Abstract
Cyperaceae species have an intrinsic value as a source of active elements with biological activity from the family of monocotyledonous known as sedges. Sedges grow in all types of soils associated with wetlands or poor soils. The aim of this present study is to evaluate the content of phenolic compounds by qualitative and quantitive analysis on Lepironia articulata. Dried leaves of L. articulata were successfully extracted by using water extraction then separated with different solvent polarities; petroleum ether, ethyl acetate and butanol fractions before being analysed using GCTOF-MS, microplate reader and HPLC. The result from the GCTOF-MS analysis of fractional extracts showed that 48 compounds were found in petroleum ether, ethyl acetate and butanol extracts. From those extracts, only six phenolic compounds were identified in ethyl acetate and butanol extracts which were 2-Methoxy-4-vinylphenol, Phenol, 2,4-bis(1,1-dimethylethyl)-, 4-Hydroxybenzaldehyde, Catechol, Phenol, 2-methoxy- and Vanillin. The total phenolic content was found to be 984.63 ± 5.96 μg GAE/g DW in L. articulata. Quantitative analysis of individual phenolic acid by HPLC showed the predominant amount of Vanillic acid (0.48 ± 0.00 μg/g DW) in ethyl acetate while 4-Hydroxybenzoic acid and Caffeic acid, both of which were 0.12 ± 0.00 μg/g DW in butanol extracts. In the present study, the plant extracts demonstrated the highest phenolic compound detected in ethyl acetate and butanol.
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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 | 0.000 |
| 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".