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Record W2055729159 · doi:10.1094/cchem.2000.77.1.70

Effects of Commercial Hydrolytic Enzyme Additives on Canadian Short Process Bread Properties and Processing Characteristics

2000· article· en· W2055729159 on OpenAlexaffabout
O. Harada, E. D. Lysenko, K.R. Preston

Bibliographic record

VenueCereal Chemistry · 2000
Typearticle
Languageen
FieldNursing
TopicFood composition and properties
Canadian institutionsCanadian International Grains Institute
Fundersnot available
KeywordsCellulaseChemistryFood scienceAmylaseEnzymeSofteningHydrolysisProteasesBread makingBiochemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

ABSTRACT The effects of increasing levels of eight commercial enzymes representing four types of fungal hydrolytic enzymes (α‐amylases, proteases, xylanases, and cellulases) on Canadian short process (CSP) bread quality and processing characteristics were studied. Addition of all enzymes types at optimum levels resulted in increased loaf volume and bread score and softer crumb. All four types of enzymes appeared to be equally effective in improving bread properties compared with the controls. At high levels, greater tolerance to the addition of xylanases and cellulases compared with the addition of α‐amylases and proteases was apparent. Mixing requirements increased with increasing levels of α‐amylase but no change was apparent with the other enzymes. Addition of all enzymes reduced sheeting work requirements, indicating a dough softening effect. Optimum bread properties for all enzymes were attained within a relatively narrow range of dough sheeting work values, which presumably correspond to optimum dough handling properties. The similarity in response of bread and sheeting characteristics at optimum levels of addition for all four enzyme types suggests a common nonspecific mechanism for improver action that is probably related to water release and the resulting impact on physical dough properties.

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 score0.722
Threshold uncertainty score0.678

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 head0.013
GPT teacher head0.222
Teacher spread0.209 · 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

Citations39
Published2000
Admission routes2
Has abstractyes

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