<i>In vitro</i> protein digestibility of direct‐expanded chickpea–sorghum snacks
Bibliographic record
Abstract
Abstract Blending cereals with pulses provides a balanced protein with higher biological value as their amino acid compositions are complementary. Extrusion not only can improve protein digestibility but also may reduce essential amino acid content. This study investigated the effects of extrusion parameters and blend ratio on in vitro protein digestibility (IVPD) and IVPD‐corrected amino acid score (IVPDCAAS) of direct‐expanded chickpea–sorghum snacks. Chickpea–sorghum blends (50:50, 60:40, and 70:30 chickpea:sorghum, w/w) were extruded at 10 combinations of moisture content (16%, 18%, and 20%) and barrel temperature (120°C, 140°C, and 160°C), and at 169°C and 15% moisture, the conditions identified in a previous study as producing maximal expansion. Chickpea and sorghum flours were extruded at 140°C and 18% moisture for comparison purposes. The IVPD of raw 50:50, 60:40, and 70:30 chickpea–sorghum blends ranged from 76% to 78%; values for raw chickpea and sorghum flours were 79% and 74%, respectively. Extrusion increased IVPD (P < 0.05) of all flours and blends. An increase in extrusion temperature increased the IVPD of extrudates (P < 0.05), whereas an increase in moisture content had the opposite effect (P < 0.05). The IVPDCAAS of raw 50:50, 60:40, and 70:30 chickpea–sorghum blends were 0.64, 0.72, and 0.73, respectively; values for raw chickpea and sorghum flours were 0.74 and 0.27, respectively. Extrusion increased IVPDCAAS (P < 0.05). The 70:30 chickpea–sorghum blend extruded at the maximal expansion exhibited the highest protein quality indicating this to be the optimal condition for snack production.
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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 | 0.000 | 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 | 0.001 |
| 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".