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Record W2068132459 · doi:10.1080/14786419.2014.986125

Identification and determination of flavonoids, carotenoids and chlorophyll concentration in <i>Cynodon dactylon</i> (L.) by HPLC analysis

2014· article· en· W2068132459 on OpenAlexaff
Saradha Devi Muthukrishnan, Kaliyaperumal Ashokkumar, Annapoorani Subramaniyan

Bibliographic record

VenueNatural Product Research · 2014
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCynodon dactylonMyricetinKaempferolRutinQuercetinChemistryFlavonoidChlorophyll bHigh-performance liquid chromatographyChlorophyllZeaxanthinViolaxanthinCarotenoidBotanyChromatographyLuteinFood scienceBiologyBiochemistryAntioxidant

Abstract

fetched live from OpenAlex

Cynodon dactylon (L.) is a potent medicinal plant in the traditional and current Indian medicinal systems. The objective of this research was to find out the levels of flavonoids, carotenoids and chlorophyll b in C. dactylon leaves by high-performance liquid chromatography (HPLC) equipped with a diode array detector. HPLC analysis revealed that total carotenoid and total flavonoid concentration were 62 mg/100 g and 249.1 μg/g, respectively. The mean chlorophyll b was 85.1 mg/100 g in C. dactylon. Among the flavonoids, quercetin (164.7 μg/g) was the major flavonoid followed by kaempferol (48.2 μg/g), rutin (18.4 μg/g), catechin (12.1 μg/g) and myricetin (5.7 μg/g). Of the carotenoids, β-carotene (35.2 mg/100 g) was predominant followed by lutein (17.0 mg/100 g), violaxanthin (5.8 mg/100 g) and zeaxanthin (4.2 mg/100 g). Chlorophyll b concentration was 85.1 mg/100 g in C. dactylon. The results of this investigation should be useful information for further pharmacological studies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.327
Teacher spread0.312 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2014
Admission routes1
Has abstractyes

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