Effect of barrel temperature and feed moisture on the physical properties of chickpea, sorghum, and maize extrudates and the functionality of their resultant flours—Part 1
Bibliographic record
Abstract
Abstract Background and objectives The effect of barrel temperature (120 and 150°C, held constant in zones 4–6) and feed moisture (20 and 24%) on the physical properties of chickpea, sorghum, and maize extrudates and the functionality of their resultant flours was investigated. Findings The moisture–temperature interaction significantly affected expansion ratio for all extrudates. In general, greater expansion occurred at 150°C; however, greater expansion occurred for chickpea and the cereal flours at high and low moisture levels, respectively. Expansion was negatively correlated with both hardness and bulk density. Extrusion also gelatinized starch, which was indirectly reflected by the 2–3× increase in water hydration capacity and 8‐40x decrease in pasting viscosities for all flours. Oil holding capacity remained relatively unchanged. Extrusion showed minor and mixed results on the emulsifying abilities of the flours, whereas all precooked flours were nonfoaming. The precooked flours also displayed reduced nitrogen solubility relative to the raw flours. Conclusions Extrusion significantly affected the hydration and pasting properties of the flours. Maximum extrudate expansion required different extrusion conditions for chickpea flour as compared to cereal flours. Significance and novelty Based on their functionality, the extruded flours have potential application in instant hot or cold beverages or may be blended with other flours to achieve the desired product functionality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".