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Record W2582537966 · doi:10.5539/jfr.v6n2p1

Wheat Bran Dietary Fiber: Promising Source of Prebiotics with Antioxidant Potential

2017· article· en· W2582537966 on OpenAlexafffundvenue
Aynur Gunenc, Christina Alswiti, Farah Hosseinian

Bibliographic record

VenueJournal of Food Research · 2017
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCarleton UniversityCanadian Nutrition Society
FundersMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsFood scienceChemistryBranPrebioticNutraceuticalAntioxidantLactobacillus acidophilusBifidobacteriumTitratable acidLactobacillusDietary fiberPolysaccharideAntioxidant capacityHydrolysisProbioticBacteriaBiochemistryBiologyFermentationOrganic chemistry

Abstract

fetched live from OpenAlex

The potential of wheat bran (WB) addition as a prebiotic source were demonstrated using yogurt with probiotics (Lactobacillus acidophilus and Bifidobacterium lactis). Yogurts (with 4% WB) were significantly (P < 0.05) different in total bacterial counts (9.1 log CFU/mL), and total titratable acidity % (TTA, 1.4%) compared to controls during 28 days cold storage (4°C). Additionally, WB-total dietary fiber contents and their bound phenolic profiles were investigated as well as the antioxidant activity of WB-water extractable polysaccharides (WEP) was studied. HPLC analysis of alkaline hydrolyzed DF fractions showed that insoluble DF had higher phenolic acids (84.2%) content than soluble DF (15.8%). Also, crude-WEP showed stronger antioxidant activity compared to purified-WEP with an ORAC of 71.88 and 52.48 µmol TE/g, respectively. Here we demonstrate WB has potentials as a source of prebiotics, which may have the potentials for functional foods and nutraceutical applications.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.080
GPT teacher head0.348
Teacher spread0.268 · 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 designObservational
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

Citations14
Published2017
Admission routes3
Has abstractyes

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