Pilot scale air classification of flours from hulled and hull-less barley for the production of protein enriched ingredients
Bibliographic record
Abstract
This study explored pilot scale air classification of flours from two barley varieties: CDC Austenson (hulled) and CDC Valdres (hull-less) for the production of protein enriched food ingredients. The objective was to understand the effect of repeat milling and air classification cycles on the shift of proteins and other components including starch, β-glucan, fiber and ash. Protein enrichment was achieved for both varieties, with 2.13 and 1.75 times increase in protein content for CDC Austenson and CDC Valdres, respectively, after the first fractionation cycle. Repeat cycles led to further protein separation for both varieties, resulting in an effective protein separation efficiency of 47–52% w/w towards the fine fractions. The fine fractions were also enriched with fat and ash. Whereas, the coarse fractions were enriched in total, soluble and insoluble dietary fibers as well as β-glucan. Starch distribution varied between varieties, with CDC Austenson showing a shift towards coarse fractions, while towards fine fraction for CDC Valdres due to its smaller granule size. This study established a pilot scale process for fractionation of barley. The compositional data of these fractions will aid the development and use of barley as a food ingredient in foods including snacks (starch-rich) and meat alternatives (protein-rich).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".