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Record W2150077153 · doi:10.5539/jas.v6n4p1

Correlation Analysis Between Antioxidant Activity and Phytochemicals in Korean Colored Corns Using Principal Component Analysis

2014· article· en· W2150077153 on OpenAlexvenueno aff
Kang‐Mo Ku, Hye Suk Kim, Soon Kwon Kim, Young‐Hwa Kang

Bibliographic record

VenueJournal of Agricultural Science · 2014
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsnot available
Fundersnot available
KeywordsAnthocyaninABTSChemistryPhenolsFood sciencePolyphenolCarotenoidAntioxidantFlavonoidColoredBotanyBiochemistryBiologyDPPH

Abstract

fetched live from OpenAlex

The colored corns are used as food as well as for feed in Asian countries; however, the active component of antioxidant activity in Korean colored corns has not been investigated. Thus, we measured the total content of carotenoids, phenols, flavonoids, and anthocyanins from 40 Korean colored corn genotypes for correlation analysis between antioxidant activity and these phytochemicals. The ferric reducing ability power (FRAP) and 2,2'-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) activity were measured in order to study this correlation. As a result, there was large variation in total anthocyanin (coefficient of variation, CV 85.0%) and total carotenoid contents (CV 87.8%), while CVs of total phenol, total flavonoid contents, ABTS and FRAP was relatively low (CV 15.0%, 22.8%, 15.5%, and 16.3% respectively). There were meaningful correlations between ABTS and anthocyanins, phenols, and flavonoids, as well as correlations between FRAP and phenols as well as FRAP and flavonoids. We also obtained a more informative and easily visualized result by using principal component analysis (PCA). Anthocyanins and carotenoids showed a large variation as compared to other compounds. Anthocyanins are a good target to increase antioxidant activity in colored corns.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
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 head0.019
GPT teacher head0.283
Teacher spread0.264 · 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 designObservational
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

Citations26
Published2014
Admission routes1
Has abstractyes

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