Functionality of Barley Proteins Extracted and Fractionated by Alkaline and Alcohol Methods
Bibliographic record
Abstract
ABSTRACT This research optimized the extraction of different protein fractions from barley grains and assessed the physicochemical properties of the fractions obtained. Pearling was first used to remove the grain's outer layers (mainly bran and germ) so that the barley cytoplasmic proteins (albumin and globulin) would be enriched in the pearling flour (PF), while endosperm proteins (hordein and glutelin) would be enriched in the pearled grain flour (PGF). Salt, alcohol, and alkaline solutions were then used to extract different barley protein fractions from PF and PGF. The effects of extraction solvent type, pH, temperature, and extraction time on protein content and extraction efficiency were studied. Aqueous ethanol (55%, v/v) efficiently extracted barley hordein from PGF at 60°C, whereas pH 11.5 alkaline solution was the most efficient for extracting both cytoplasmic and endosperm proteins from barley PF and PGF at 23°C. Subunit molecular weight, amino acid composition, and the functional properties of each isolated barley protein fraction were investigated. Barley glutelin demonstrated superior oil‐binding property and emulsifying stability, whereas barley hordein exhibited good foaming capacity.
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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) | 0.001 | 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".