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Record W2784454337 · doi:10.5539/jfr.v7n2p10

Enzymatic Kinetics of Enzymatically Extruded Degerminated Maize Using Glucamylase

2018· article· en· W2784454337 on OpenAlexvenueno aff
Chengye Ma, Shuangshuang Yu, Yuyan Fan, Shuhua Wu, Zhehao Zhang, Song YueDong, LI Hong-jun

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsKineticsChemistrySubstrate (aquarium)EnzymeEnzyme kineticsHydrolysisRate equationNuclear chemistryChromatographyBiochemistryBiologyActive site

Abstract

fetched live from OpenAlex

In this study, the reaction rates of native degerminated maize, extruded degerminated maize and enzymatically extruded degerminated maize using glucoamylase were evaluated and the extrudate models were investigated. The effects of enzyme concentration, substrate pH, temperature and incubation time on the reaction rates were studied. The Lineweaver–Burk equation was used in order to obtain the parameters of the kinetics equation of catalysed hydrolysis. The results show that NDM’s vm is 0.0845g/(mL·min) and km is 0.0032, EDM’s vm is 0.6251g/(mL·min) and km is 0.0167, EEDM’s vm is 1.897g/(mL·min) and km is 0.0240. The reaction rate of EEDM is quicker than those of NDM and EDM. The kinetics equation of EEDM is in accordance with the Michaelis–Menten equation.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.088
GPT teacher head0.391
Teacher spread0.303 · 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
Published2018
Admission routes1
Has abstractyes

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