Physical and microstructural quality of extruded snacks made from blends of barley and green lentil flours
Bibliographic record
Abstract
Abstract Background and objectives Most puffed snacks in the market are made from refined cereal flours which allow greater expansion and better texture but are nutritionally inferior as they lack protein and dietary fiber. Whole barley and green lentil flours at several blending ratios were extruded as a function of temperature and moisture content to optimize the physical and microstructural quality of fiber and protein‐enriched snacks. Findings High extrusion temperature significantly improved overall expansion and texture. The effects of feed moisture depended on the blending ratio, parallel with the total protein and dietary fiber content of extrudates. Barley:green lentil of 45:55 showed the highest extrudate expansion (~1.9 mm/mm on average) and lowest hardness (~29 N on average), followed by the blend 60:40. X‐ray microtomography showed that this blending ratio also produced a larger mean cell size (~1.6 mm), lower mean wall thickness (~0.3 mm), and higher overall connectivity between cells. Conclusion Barley and green lentil when blended at the ratios of 45:55 and 60:40, and extruded at higher temperature resulted in optimal extrudate physical and microstructural properties including higher expansion and crispness, thinner cell walls, reduced hardness, and crunchiness. Significance and novelty Blending cereal and pulse flours in snack food applications will allow development of fiber and protein‐enriched options that are also texturally and structurally appealing.
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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 | 0.000 |
| 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".