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Record W2070334971 · doi:10.1016/j.lwt.2012.04.030

Effects of debranning on the distribution of pentosans and relationships to phenolic content and antioxidant activity of wheat pearling fractions

2012· article· en· W2070334971 on OpenAlexafffund
H. D. Sapirstein, Ming‐Wei Wang, Trust Beta

Bibliographic record

VenueLWT · 2012
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBranFerulic acidFood scienceNutraceuticalComposition (language)ChemistryAntioxidantCultivarFraction (chemistry)Dietary fiberBotanyBiologyBiochemistryChromatographyRaw materialOrganic chemistry

Abstract

fetched live from OpenAlex

Pentosans represent the major dietary fiber constituent in wheat and the predominant source of antioxidant activity (AOA) whose nature is closely associated with the phenolic compound ferulic acid. Incremental debranning was used to determine effects on pentosan content and AOA of pearled fractions. Four cultivar samples were debranned to obtain pearling fractions, each equivalent to 5% of initial sample weight up to 60%. Total pentosans (TP), water-extractable pentosans (WEP), total phenolic content (TPC) and AOA were determined. There were significant differences among genotypes in response to debranning although trends were consistent. WEP was maximized in the 10% debranning fraction which likely maximized aleurone content. TP content was highest in the initial 5% fraction and progressively declined in successive fractions. Relationships between pentosan composition and AOA followed different trends. TP and water-unextractable pentosans (WUP) were highly correlated to TPC (R2 = 0.84) and AOA (R2 = 0.88) for the 10–25% debranning fractions. No significant correlations were found between TPC and AOA with WEP. Results indicated that debranning wheat to recover material between the initial 5% pearling fraction and the next 5% was effective to produce bran fractions for functional food or nutraceutical purposes, highly enriched in dietary fiber and AOA.

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 score0.404
Threshold uncertainty score0.150

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 head0.059
GPT teacher head0.266
Teacher spread0.207 · 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

Citations18
Published2012
Admission routes2
Has abstractyes

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