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Record W2090640341 · doi:10.1094/cc-82-0144

Application of Polished‐Graded Wheat Grains for Sourdough Bread

2005· article· en· W2090640341 on OpenAlexaboutno aff
Ji Hyun Kim, Tomoko Maeda, Naofumi Morita

Bibliographic record

VenueCereal Chemistry · 2005
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsnot available
Fundersnot available
KeywordsFood scienceBread makingChemistryGlutenBranWheat flourYeastLactic acidFermentationTitratable acidStarchFermentation starterBacteriaBiochemistryRaw materialBiology

Abstract

fetched live from OpenAlex

ABSTRACT Flours obtained by a specific polishing process were used to prepare sourdough and bread. Three fractions designated C‐1 (100–90%), C‐5 (60–50%), and C‐8 (30–0%) were studied. The pH, total titratable acidity levels, and buffering capacity of sourdoughs made from polished flours were significantly different from those of the control sourdough with No. 1 Canada Western Red Spring (CW), and they provided sourdough breads with better qualities than that of CW. The growth of lactic acid bacteria and yeast in polished flour sourdoughs were significantly accelerated during fermentation over that in CW sourdough. Higher maturation of polished flour sourdoughs softened the hardness of mixed dough. The intricate network of honeycomb structure gluten and uneven surface of starch granules were distinctly observed in SEM images. Substitutions of C‐5 or C‐8 sourdoughs for CW significantly increased the loaf volume and softened breadcrumbs more than CW sourdough. Flour qualities of polished flours such as suitable acidity and good buffering capacity caused by the bran fraction were effective for better growth and longer life of yeast in the dough during fermentation. Therefore, application of polished flours in sourdough bread would improve rheological properties of dough and bread as compared with CW sourdough.

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 score0.062
Threshold uncertainty score0.382

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 head0.015
GPT teacher head0.261
Teacher spread0.246 · 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
Published2005
Admission routes1
Has abstractyes

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