Effects of micronization on the physicochemical and rheological properties of wheat varieties
Bibliographic record
Abstract
A more comprehensive understanding of micronization (infrared treatment) has become necessary due to the potencial use of this technique for processing grains and seeds.The main objective of this research was to investigate the effects of micronization, at different moisture levels, on physical, chemical, and rheological properties of wheats.Four wheat varieties (AC Karma, AC Barrie, Glenlea, and AC lvory) were subjected to infrared radiation, at three moisture levels (as is, 16%, and 22%), to reach an average layer temperature of 100 t 5"C.The wheat samples were then milled and analyzed for single kernel characteristics, flour yield, ash and total protein content, protein fraction cha racteristics, alpha-amylase activity, and rheological behaviors.The protein fractionation test revealed significant decreases (p < 0.01) in both monomeric proteins (from 54% of total protein in the control to 37o/o in the tempered micronized sample) and soluble glutenins (from 9.4 to 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 22o/o moisture decreased 43.5% compared with non- micronized control samples using sE-HPLc.Micronization had a remarkable effect on the gluten properties as seen from the significant decreases of water absorption (P < 0.01) and dough development time (P < 0.0.1 ).Results suggest that micronization to high temperature level has detrimental effects on gluten functionality by decreasing protein solubility and impairing physicochemical and rheological propedies of wheat flour.This behavior is largely independent of wheat varieties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".