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Record W2025784885 · doi:10.1094/cchem-04-11-0043

Thermostability of Barley Malt Proteases in Western Canadian Two‐Row Malting Barley

2011· article· en· W2025784885 on OpenAlexaffabout
Syed Masood Hasan Rizvi, Aaron D. Beattie, B. G. Rossnagel, G. J. Scoles

Bibliographic record

VenueCereal Chemistry · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Production and Characterization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsThermostabilityProteasesProteaseChemistryMashingHydrolysisBiochemistryGerminationEnzymeAmino acidEndospermFood scienceBotanyBiology

Abstract

fetched live from OpenAlex

ABSTRACT During the malting process, barley is germinated via a carefully controlled procedure so that its components are degraded to sugars, amino acids, and other low molecular weight compounds that can be used for subsequent fermentation. One of the most important of these processes is the hydrolysis of proteins into peptides and amino acids. During seed germination, proteases hydrolyze insoluble reserve proteins into soluble peptides that are subsequently hydrolyzed into free amino acids. During kilning, green malt is initially air dried at 40–60°C, and then the temperature is gradually increased to 85–95°C. Although most proteases are denatured during kilning, the malt contains a small proportion of heat‐stable protease enzymes able to further break down protein in the subsequent mashing process. In this study, protocols were developed and standardized to measure the activity of different proteases. These protocols were then used to study protease thermostability in Canadian two‐row spring malting barley lines. We found a wide range in protease activity under controlled conditions. Upon heat treatment, several lines exhibited significant protease thermostability. These thermostable enzymes were purified by ammonium sulfate precipitation and Sephadex columns for further study.

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 score0.165
Threshold uncertainty score0.996

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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