Effects of Micronization on Protein and Rheological Properties of Spring Wheat
Bibliographic record
Abstract
ABSTRACT This research investigated the effects of micronization, at different moisture levels, on the chemical and rheological properties of wheat. A set of tests designed to analyze protein fraction characteristics and rheological behaviors were conducted on samples from four wheat cultivars (AC Karma, AC Barrie, Glenlea, and Kanata). After being subjected to infrared radiation at three moisture levels (as‐is, 16%, and 22%), the seeds were milled to produce straight‐grade flour. The protein fractionation test revealed significant decreases (P ≤ 0.01) in both monomeric proteins (from 54% of total protein in the control to 37% in the tempered micronized sample) and soluble glutenins (9.4–2.5%). There was a strong negative correlation (r = ‐0.98) between the percentages of monomeric proteins and insoluble glutenins. Total extractable proteins of micronized samples tempered to 22% moisture decreased 43.5% when compared with nonmicronized control samples using size‐exclusion HPLC (SE‐HPLC). Micronization had a significant effect on gluten properties, as seen from a decrease in water absorption (P ≤ 0.01) and dough development time (P ≤ 0.01). Results showed that micronization at 100 ± 5°C had detrimental effects on wheat flour gluten functionality, including a decrease in protein solubility and impairment of rheological properties. These phenomena could be due to the formation of both hydrophobic and disulfide bonds in wheat during micronization.
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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".