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Record W2393909738

Protein Fractions of Wheat and Their Relationships with Noodle Quality

2004· article· en· W2393909738 on OpenAlexaboutno aff
Shaohui Ouyang, Chun Wang

Bibliographic record

VenueZhongguo nongye Kexue · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsnot available
Fundersnot available
KeywordsGluteninFood scienceChemistryCommon wheatAgronomyBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The protein fractions quantitative of 25 Chinese wheat varieties were studied with a new protein extractionmethod, together with the relationships between protein fractions and other protein indexes, dough property, and freshnoodle quality were also studied. The average ratios of monomeric protein, soluble glutenin and insoluble glutenin of wheatin China's Huanghuai winter wheat zone was 3.7∶1.0∶1.8. Compared with Canadian wheat varieties, the monomericprotein was lower, the soluble glutenin content was higher and the insoluble protein was lower, may be it is the mainlydifference between noodle wheat and bread wheat. The monomeric protein has lesser important contribution to the noodleextension quality than glutenin. The insoluble glutenin and soluble glutenin content are significantly correlated with part ofthe rheology parameters and other protein quality index, such as sedimentation volume and swelling index of glutenin, andplay an important role to fresh noodle extension characters, for example, the fresh noodle sheet thickness and length, freshnoodle maximum resistance of extension, extension distance and extension area. The results suggested that the monomericprotein is less important than that of the glutenin for fresh noodle resistance to extension; soluble glutenin content is themost important property for noodle special wheat, and soluble glutenin content can be used for to screen Chinese noodlespecial wheat in the early stages.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.196

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 head0.042
GPT teacher head0.238
Teacher spread0.196 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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