Extrusion texturization of air-classified barley protein: a sustainable plant-based meat alternative
Bibliographic record
Abstract
Introduction: Barley’s share as human food has remained limited to ~3% of its production, with most of it utilized for animal feed. High protein barley cultivars have potential to become an attractive novel plant-based food ingredient in growing protein ingredient space. Plant-based alternatives, aimed to substitute meat consumption emerge as relatively sustainable alternatives catering to the ever-increasing meat consumption pattern. The present study addresses the sustainability needs by partially replacing highly refined protein ingredients with relatively sustainable dry fractionated protein-rich fraction from barley. Methods: Firstly, a pilot-scale air classification process was employed to fractionate barley flour from two varieties i.e., CDC Valdres and CDC Austenson. Significant protein enrichment, with 2.13-fold increase (27.1% db) for CDC Austenson and a 1.75-fold increase (26.3% db) for CDC Valdres compared to their original feed flour protein contents was obtained. Secondly, these protein enriched barley fractions were used for meat analogues development by blending them with pea protein isolate at 15% and 30% w/w barley inclusion. The blends were subjected to a high-moisture extrusion, at three different moisture contents (47.5%, 52.5% and 57.5% w/w). Next, the physical properties such as density, color, texture (cutting strength, hardness, chewiness, gumminess and springiness), and techno-functionality (water and oil holding capacity) of the meat analogues were analyzed. Results: Protein-rich barley fraction inclusion up to 30% produced sufficiently texturized product for each variety, with barley containing meat analogues indicating better visual textural characteristics than those made from pea protein alone. Meat analogues produced at 30% protein-rich barley fraction substitution had significantly higher values for hardness, chewiness, and gumminess (p<0.05) compared to pea protein alone. However, increase in feed moisture content resulted in lower values for these textural attributes. Therefore, through variation in feed moisture content and formulations, meat analogues with a wide range of textures were obtained. Conclusion: This study provided a basis for barley’s fractionation and inclusion of its protein enriched fractions into plant-based meat analogues. Future work can be focused on comprehensive sensory evaluations and assessment of consumer acceptability of barley-based meat alternatives.
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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) | 0.001 | 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) | 0.000 | 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".