SME‐Arrhenius Model for WSI of Rice Flour in a Twin‐Screw Extruder
Bibliographic record
Abstract
ABSTRACT We have modeled a rice extrusion process focusing specifically on the starch gelatinization and water solubility index (WSI) as a function of extrusion system and process parameters. Using a twin‐screw extruder, we examined in detail the effect of screw speed (350–580 rpm), barrel temperature, different screw configurations, and moisture content of rice flour on both extrusion system parameters (product temperature, specific mechanical energy [SME], and residence time distribution [RTD]) and extrudate characteristics (expansion, density, WSI, and water absorption index [WAI]). Changes in WSI were monitored to reveal a relationship between the reaction kinetics during extrusion and WSI. Reaction kinetics models were developed to predict WSI during extrusion. WSI followed a pseudo first‐order reaction kinetics model. It became apparent that the rate constant is a function of both temperature and SME. We have developed an adaptation of the kinetic model based on the Arrhenius equation that shows better correlations with SME and distinguishes data from different screw configurations. This adaptation of the model improved predictability of WSI, thereby linking the extrusion conditions with the extruded product 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".