Rheological Characteristics of Arabic Gum in Combination With Guar and Xanthan Gum Using Response Surface Methodology: Effect of Temperature and Concentration
Bibliographic record
Abstract
A rheological characterization of combination guar and xanthan gum with 20 kg/100 kg sample arabic gum was performed at 20 to 80°C by the application of the response surface methodology using an advanced controlled rate rheometer. The guar and xanthan gum concentrations employed were 0.25-1.25 kg/100 kg sample. The flow of both combinations was adequately described by Herschel-Bulkley model over the shear rate range of 0-500 s−1. The combination of arabic-guar exhibited shear-thinning behavior while arabic-xanthan combination behaved as a dilatants fluid with yield stress. A quadratic model developed for rheological parameters met all the criteria of good fit and provided useful information. It was observed that temperature and concentration affected yield stress, consistency coefficient and apparent viscosity (P<0.05) of gum combinations however, flow behavior index did not. The concentration of gum significantly (P<0.05) affected all the rheological parameters and temperature was the least. Addition of arabic gum significantly (P<0.05) reduced rheological properties of both guar and xanthan gum.
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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 | 0.001 | 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) | 0.001 | 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".