Assessment of Effects of Extrogenous Proteins on the Thermomechanical and Dynamic Rheological Properties of Oat Dough Using Mixolab and Rheometer
Bibliographic record
Abstract
The thermomechanical and rheological properties of oat dough with different levels of extrogenous proteins (gluten, soy protein, egg albumin) were mainly analyzed using the Mixolab and the Rheometer in the present study. The Mixolab results showed that gluten and soy protein could significantly (p0.05) increase the water absorption of the doughs, while egg albumin reduced it. All three extrogenous proteins may shorten the dough development time, and the gluten was the most effective, while soy protein had the least influence. Compared to the soy protein-oat dough, the dough with gluten or egg albumin had a longer period of stability, especially at the levels of 10% and 15%, respectively. The highest peak torque and setback were observed in the dough containing egg albumin when the starch gelatinized, and the increased addition of egg albumin resulted in the increases of the two parameters, whereas soy protein addition decreased both of the parameters and gluten addition showed no significant effects. The elastic modulus (G') and viscous modulus (G) of the dough with gluten or soy protein that recorded in the oscillatory tests drastically increased as the increase of addition level, but egg albumin led to the contrary results. DSC analysis showed that egg albumin and soy protein significantly (p0.05) increased the peak temperature (Tp) and the conclusion temperature (Tc), and also increased the enthalpy (ΔH) to a certain extent, while gluten showed no significant effects.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".