Effect of extrusion conditions on the physical properties of desi chickpea‐barley extrudates and quality attributes of their resulting flours
Bibliographic record
Abstract
In this study, response surface methodology (RSM) was used to evaluate the effect of extrusion conditions on physical properties of chickpea:barley extrudates (60:40), and the resulting protein quality of their flours. Barrel temperature (150-170°C) and moisture content (16-20%) were chosen as independent variables to generate a central composite design. Hardness, expansion index, bulk density, and protein quality were analyzed as responses parameters. Expansion was found to be higher at lower temperatures and higher moisture for the 60:40 chickpea:barley blend; bulk density became reduced with increased moisture; and hardness was found to increase at higher temperatures and lower moistures. The protein quality of their resulting flours was found to be greater at moisture contents higher than 16%. The composition, protein quality, and functional attributes were also examined for raw and precooked flours of chickpea, barley, and their blend at the center point of the RSM design (18% moisture, 160°C). Extrusion also leads to improved water hydration capacities and reduced viscosities for precooked individual and blended flours relative to the raw. Moreover, extrusion also led to improved protein quality in the chickpea and chickpea-barley blend, but not the individual barley flour.
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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) | 0.000 | 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".