Effects of Commercial Hydrolytic Enzyme Additives on Canadian Short Process Bread Properties and Processing Characteristics
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".