The Combination of Enzymes in the Enhancement of Fibre-Enriched Product Quality: Effects of the Interactions of Dietary Fibre, Gluten Proteins, and Starch Granules on Dough Rheological Properties and Bubble Dynamics
Bibliographic record
Abstract
Due to the presence of insoluble dietary fibre (IDF), DF-enriched products have a lower consumer acceptance compared to those prepared using a regular formulation. The objective of this review was to focus on a comprehensive utilization of enzymes for improving the DF-enriched dough rheology and bubble dynamics via the regulation of intermolecular interactions between DF, starch granules, and gluten proteins. Xylanase was used to promote the interactions between water-extractable arabinoxylan (WEAX) and gluten proteins, leading to a stronger gluten network and dough liquid film around gas bubbles. Cellulase was applied to promote the breakdown of cellulose, mitigating the adverse impacts of IDF on the gluten structure. Glucose oxidase (GOx) was utilized to facilitate disulfide bond (S-S) formation between gluten proteins, thereby enhancing the gluten strength, gas retention capacity, and bubble stability of dough during processing. Amylase incorporation promoted bubble expansion of high-fibre dough. In conclusion, the review established a solid theoretical framework on how an unpredictable evolution for the rheological behaviour and bubble dynamics of dough during processing could be modified via the complicated interactions involving enzymes and biopolymers. This will contribute to a high-quality development for the fibre-enriched product industry, and also a sustainable promotion of regular DF consumption.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".