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Record W4385065275 · doi:10.1002/cche.10702

A research on milling fractions of biofortified and nonbiofortified hull‐less oats in terms of minerals, arabinoxylans, and other chemical properties

2023· article· en· W4385065275 on OpenAlexaff
Oğuz Acar, Marta S. Izydorczyk, Tricia McMillan, Mustafa Atilla Yazıcı, A. İmamoğlu, İsmail Çakmak, Hamit Köksel

Bibliographic record

VenueCereal Chemistry · 2023
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryBranFood scienceBiofortificationArabinoxylanBioavailabilityDietary fiberMineralFiberRaw materialPolysaccharideMicronutrientBiochemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Abstract Background The aim of the study was to investigate the arabinoxylan (AX) content, yield, recovery, and enrichment factors of milling fractions obtained from short‐ and long‐flow milling from biofortified (+) and nonbiofortified (−) oats cv. Haskara by determining the monosaccharide components besides dietary fiber and mineral contents. Findings Coarse brans (CB) obtained by short‐flow milling from Haskara (+) and (−) samples had around 3.3% AX contents and 1.5 enrichment factor while fine brans (FB) obtained by long‐flow milling had around 3.8% AX contents and enrichment factors higher than CB. The differences between bran samples of Haskara (+) and Haskara (−) were significant ( p < .05) in terms of insoluble dietary fiber. CB of Haskara (+) had 1.5, 2.9, and 3.8 times higher Zn, I, and Se contents than those of whole grain Haskara (−) while FB of Haskara (+) had 1.7, 4.7, and 3.7 times higher Zn, I, and Se contents than those of whole grain Haskara (−), respectively. Conclusions Mineral/fiber‐rich fractions obtained by biofortification and milling applications can be used against mineral deficiency. Significance and Novelty This is a pioneering study on AX and mineral contents of fractions obtained by different milling flows from biofortified hull‐less oats.

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 score0.006
Threshold uncertainty score0.399

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 head0.133
GPT teacher head0.340
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

Citations3
Published2023
Admission routes1
Has abstractyes

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