Rheological properties of batter systems containing different combinations of flours and hydrocolloids
Bibliographic record
Abstract
The rheological properties of batters formulated using different combinations of wheat,corn and rice flours with two types of hydrocolloids namely methylcellulose (0.5, 1 and 1.5%) orxanthan gum (0.2%) were studied. Control samples were formulated with combinations of flourswithout the added hydrocolloids. The effects of hydrocolloids on the rheological flow characteristicsof the batter systems were measured using a controlled stress rheometer at a temperature of 15oC.The effects of hydrocolloids on dynamic viscoelastic parameters as functions of temperatures wereevaluated. All the batters showed shear thinning behavior with flow behavior indices in the rangefrom 0.34 to 0.67. Addition of xanthan gum lowered flow index values impacting higher degree ofpseudoplasticity to the batter samples compared to methylcellulose. Consistency index of controlbatter samples varied from 0.46 to 69.2 Pa.sn. Addition of hydrocolloids increased the consistencyindex. Xanthan gum (XG) and methylcellulose (MC) significantly increased consistency index valuesof batters. The gums changed the onset temperature of structure development, and storage (Gmax)and loss moduli (Gmax) of the batter systems. However, no effect was observed on peak temperaturein which the G reached maximum value. Xanthan gum increased both Gmax and Gmax whereas athigher concentrations, methylcellulose increased Gmax but lowered Gmax.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".