Effects of Pearling, Grinding Conditions, and Roller Mill Flow on the Yield and Composition of Milled Products from Hull‐less Barley
Bibliographic record
Abstract
ABSTRACT Two hull‐less barley cultivars, one with waxy starch and the other with high‐amylose starch, were roller‐milled unpearled and after 15% pearling. Flows of varying length, with diverse roll settings and roll surfaces were used to determine effects on the yield, composition, and properties of milled products. Similar trends were noted for the two cultivars. When using a short flow comprising four break passages and a sizing passage, power consumption during grinding was reduced by 10% when roll flute orientation was changed from dull‐to‐dull (D/D) to sharp‐to‐sharp (S/S). Flute orientation had minimal effects on the yield and brightness of flour, but SS grinding gave a higher yield of a fiber‐rich fraction (FRF). FRF yield and composition are of particular interest because FRF has potential as a functional food ingredient due to elevated levels of β‐glucans (BG) and arabinoxylans (AX). When using smooth frosted rolls (SM) for the sizing passage, power consumption increased by several times over using fluted sizing rolls with little advantage for either yield or BG content of FRF. FRF starch damage increased when smooth sizing rolls were used, and water swelling, a measure of water hydration capacity, also increased. Setting break and sizing rolls sharp‐to‐sharp significantly lowered the mean particle size of the FRF fraction, accompanied by moderate declines in FRF BG and AX contents. FRF yield decreased ≤50% when milling flow was lengthened to three sizing passages with intermediate impact passages, with only a moderate accompanying increase in FRF fiber content, regardless of roll conditions. Pearling 15% before milling reduced the yield of FRF by ≈30% while moderately reducing flour yield. Flour brightness was improved by pearling. When barley was pearled, FRF contained higher amounts of BG, but lower amounts of AX, phenolics, ash, and protein.
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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) | 0.000 | 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) | 0.000 | 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".