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Record W2084353981 · doi:10.1094/cchem-01-10-0010

<i>C</i>‐Glycosylflavone and Lignan Diglucoside Contents of Commercial, Regular, and Whole‐Wheat Spaghetti

2011· article· en· W2084353981 on OpenAlexaff
Rhanissa Hirawan, Trust Beta

Bibliographic record

VenueCereal Chemistry · 2011
Typearticle
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsFood scienceChemistryLignanApigeninBranWhole grainsFlavonoidWhole wheatHealth benefitsGlycosideRaw materialAntioxidantBiochemistryOrganic chemistryTraditional medicine

Abstract

fetched live from OpenAlex

ABSTRACT Consumption of whole‐wheat products, including whole‐wheat spaghetti, is associated with beneficial health effects. Flavonoids and lignans are antioxidant phytochemicals that have received much attention from researchers. Investigations were conducted on the content of flavonoid glycosides, lignan diglucoside, and secoisolariciresinol diglucoside (SDG) as contributors to the health‐promoting properties of whole‐wheat spaghetti. Flavonoid glycosides present in regular and whole‐wheat spaghetti samples were identified as 6‐ C ‐glucosyl‐8‐ C ‐arabinosyl apigenin and the sinapic acid ester of apigenin‐ C ‐diglycoside while, in a previous study, the sinapic acid ester of apigenin‐ C ‐diglycoside was found only in wheat germ tissues. The content of these compounds was significantly higher in whole‐wheat spaghetti (17.0 and 15.1 μg of apigenin equivalent/g) compared to the regular brands (9.5 and 5.8 μg apigenin equivalent/g). SDG content was also significantly higher in whole‐wheat spaghetti (41.8 μg/g) compared to the regular brands (12.9 μg/g). These findings lend further support to the notion that phenolic compounds, along with dietary fiber, are concentrated in the bran layers of the wheat kernel; hence, consumption of whole grain products is strongly recommended to obtain significant levels of health‐promoting phytochemicals.

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 score0.027
Threshold uncertainty score0.568

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 head0.029
GPT teacher head0.229
Teacher spread0.199 · 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

Citations10
Published2011
Admission routes1
Has abstractyes

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