Pelargonium graveolens: Towards In-Depth Metabolite Profiling, Antioxidant and Enzyme-Inhibitory Potential
Bibliographic record
Abstract
Pelargonium graveolens L’Hèr. (Geraniaceae) is renowned for its traditional use as a flavor, ornamental and medicinal plant. This work aimed at an in-depth study of the phytochemical profiling and in vitro antioxidant and enzyme inhibition assessment of a methanol-aqueous extract from P. graveolens leaves. A UHPLC-HRMS analysis revealed more than 110 secondary metabolites, including 8 acyltartaric and 11 acylcitric/acylisocitric acids; 8 gallotannins; 36 flavonols, flavanones and methoxylated flavonoids together with 17 phenolic and aliphatic acids; and 21 phenolic acid glycosides. For the first time, acylcitric acids along with feruloyl- and coumaroyltartaric acids are reported in the species. The leaf extract actively scavenged 2,2-diphenyl-1-picrylhydrazyl DPPH (273.45 mg trolox equivalent (TE/g)) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+) radicals (531.97 mgTE/g) and showed a high reducing power: 431.32 mg TE/g Cupric reducing antioxidant capacity (CUPRAC) and 292.21 mg TE/g Ferric reducing antioxidant power (FRAP). It possessed a metal chelating capacity (13.44 ethylenediaminetetraacetic acid equivalent (EDTAE)/g) and contained 2.71 mmol TE/g in the phosphomolybdenum assay. The rose geranium extract exhibited high inhibition towards acetyl- and butyrylcholinesterase (2.80 and 2.20 mg galantamine equivalent (GALAE)/g, respectively) and tyrosinase (75.49 mg kojic acid equivalent (KAE)/g). It inhibited α-glucosidase and α-amylase (3.75 mmol and 0.79 acarbose equivalent (ACAE)/g, respectively) and lipase (28.91 mg orlistat equivalent (OE)/g). This study sheds light into the future potential application of the rose geranium in pharmaceutical and nutraceutical products.
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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".