Development of protein-Rich Sorghum-Based Expanded Snacks Using Extrusion Technology
Bibliographic record
Abstract
Sorghum is an important staple crop in semi-arid regions of Africa and India because of its drought tolerance. But low protein content and quality limit its widespread use. This project focused on developing sorghum-based extruded snacks. Results from preliminary lab-scale extrusion experiments were used to design a 2×5 factorial pilot-scale study. Two blends of sorghum flour and corn flour were prepared (6:1 and 5:2 w/w ratios) as the controls. Three different sources of protein—whey protein isolate, defatted soy flour, and mixed legume flour—were added to the sorghum/corn flour blends at 30%. A 50:50 blend of defatted soy flour and whey protein isolate was also added at 30% to the sorghum/corn flour blends. The resultant ten formulations were extruded on a pilot-scale twin-screw extruder to investigate the effects of sorghum/corn flour ratio and protein addition on product expansion, microstructure, mechanical properties, and sensory attributes. Expansion ratio of extruded product increased at the higher level of corn flour, and decreased with the incorporation of protein sources. Extrudates with defatted soy flour had a lower expansion ratio (5.3–5.4) than those with whey protein isolate (7.7–7.9), legume flour (7.1–9.9), or whey protein isolate-defatted soy flour (6.1–6.9). Extrudate microstructure, obtained by X-ray microtomography, corresponded well with expansion characteristics. Extrudates with defatted soy flour had the lowest cell diameter. Average crushing force (ranging from 40.9 to 154.87 N) was lower for extrudates with a higher level of corn flour. However, contrary to expectations, crushing force and crispness work both decreased with incorporation of protein sources. Consumer acceptability results showed that the addition of protein sources enhanced taste and overall acceptability of the extruded snacks, with the treatment sorghum/corn flour 5:2 and whey protein isolate-defatted soy flour as the protein source having significantly higher ratings than the other treatments.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".