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Record W6958208563 · doi:10.60692/dff35-pvt98

Determination of essential oil and chemical composition of St. John's Wort

2024· article· en· W6958208563 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNatural Compound Pharmacology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHypericum perforatumGallic acidEssential oilSabineneAntioxidantTroloxQuercetin

Abstract

fetched live from OpenAlex

Abstract Considering it contains a variety of physiologically active compounds, including flavonoids, common phenols, and essential oils (EOs), St. John's wort ( Hypericum perforatum L.) is a common plant in Bulgaria that is predominantly used in folk medicine to cure various disorders. Determining the chemical makeup of St. John's wort inflorescences that were gathered from northern Bulgaria was the purpose of this investigation. The antioxidant activity of H. perforatum L. extracts was assessed using 1,1-diphenyl-2-picrilhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), and Trolox equivalent antioxidant capacity (TEAC) tests on methanol extract. The amount of EO obtained by water distillation was 0.08%, with its main components (over 3%) being n -nonane (27.46%), β -sesquiphellandrene (11.17%), heptanal dimethyl acetal (5.94%), ethyl hexyl ketone (5.93%), undecane (3.75%), sabinene (3.3%), and tridecyl alcohol (3.1%). Methanol extracts were obtained from the inflorescences, with the total flavonoid content determined as 8.66 mg quercetin equivalents (QE)/mg and total phenolic content as 271.33 mg Gallic acid equivalent/g. The FRAP assay yielded 493.07 µmol/L of antioxidant activity, while the TEAC assay yielded 106.39 µmol/L, respectively. Our findings enable a comprehensive characterization of H. perforatum from Bulgaria and an assessment of its oil suitability for potential industrial applications. Additionally, the results could guide the selection of specimens for future targeted breeding efforts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.114

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.230
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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