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ANTIOXIDANT ACTIVITY OF COMMON BEANS (<i>PHASEOLUS VULGARIS</i> L.)

2004· article· en· W2014989058 on OpenAlexafffund
Terrence Madhujith, M. Naczk, Fereidoon Shahidi

Bibliographic record

VenueJournal of Food Lipids · 2004
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsSt. Francis Xavier UniversityMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryPhaseolusTBARSCatechinFood scienceAntioxidantTanninThiobarbituric acidAcetoneHexanalProanthocyanidinCarotenePolyphenolBotanyBiochemistryLipid peroxidation

Abstract

fetched live from OpenAlex

ABSTRACT Four bean varieties with different hull colors (white, red, brown and black) were extracted with 80% acetone and evaluated for their total, hydrophobic and hydrophilic phenolics as well as tannin contents. The tannin content was measured using three different methods. Antioxidant capacity of the extracts was assessed using β‐carotene/linoleate and bulk corn oil model systems. The content of hydrophobic antioxidants was higher compared with the hydrophilic fraction in all tested bean types. Evaluation of antioxidant activity in a β‐carotene model system revealed that red, brown and black whole bean extracts were capable of inhibiting oxidation (33–52%) of β‐carotene as compared with the control when used at 100 p.p.m. level as catechin equivalents. In the corn oil model system, red, brown and black bean extracts inhibited the formation of conjugated dienes (CD; 20–28%), thiobarbituric acid reactive substances (TBARS; 44–52%) and hexanal (68–84%) when used at 100 p.p.m. level as catechin equivalents. The results of this study demonstrated that colored beans possess superior antioxidative activity compared with white beans.

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 score0.003
Threshold uncertainty score0.006

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)0.0000.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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

Citations90
Published2004
Admission routes2
Has abstractyes

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