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Record W2016593954 · doi:10.1094/cchem-07-11-0084

Detection, Localization, and Variability of Endogenous β‐Glucanase in Wheat Kernels

2011· article· en· W2016593954 on OpenAlexaff
Azadeh Vatandoust, Sanaa Ragaee, Peter Wood, Susan M. Tosh, Koushik Seetharaman

Bibliographic record

VenueCereal Chemistry · 2011
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsAgriculture and Agri-Food CanadaMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsGlucanaseChemistryBranFood scienceCultivarPolysaccharideGlucanEnzymeSize-exclusion chromatographyArabinoxylanKernel (algebra)EndogenyGenotypeBiochemistryAgronomyBiologyGeneMathematicsRaw material

Abstract

fetched live from OpenAlex

ABSTRACT Clinical studies with isolates of β‐glucan have shown that the health benefits are regulated not only by the polysaccharide concentration but also by the molecular weight and concentration in solution, because these health benefits are controlled, inter alia, by viscosity in the gut. The degradation of β‐glucan in baked products is likely caused by baking ingredients or processes, or by endogenous enzymes in wheat flour. The objectives of the present study were to quantify β‐glucanase in wheat kernels and to determine factors that influence the levels of this enzyme. A modified protocol to quantify β‐glucanase was developed and then confirmed through high‐performance size‐exclusion chromatography (HPSEC) with Calcofluor detection. Under this protocol, it was shown that the concentration of β‐glucanase activity was the highest in the bran fraction of the kernel in ungerminated wheats, whereas it was distributed throughout the entire kernel following germination. Furthermore, investigation on different wheat cultivars planted in the same and different locations showed that genotype, environment, and agronomic practice all can have an effect on β‐glucanase activity level in wheat kernels.

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 score0.044
Threshold uncertainty score0.312

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 head0.214
Teacher spread0.185 · 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
Published2011
Admission routes1
Has abstractyes

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