Extractability and Molecular Modifications of Gliadin and Glutenin Proteins Withdrawn from Different Stages of a Commercial Ethanol Fuel/Distillers Dried Grains with Solubles Process Using a Wheat Feedstock
Bibliographic record
Abstract
ABSTRACT Extractability and molecular modifications of gliadin and glutenin proteins withdrawn from different stages of a commercial ethanol fuel/distillers dried grains with solubles (DDGS) process using a wheat feedstock were investigated. Materials were taken postliquefaction (PL), postdistillation (whole stillage), and postdrying (DDGS) during the process and then fractionated to separate the gliadins and the soluble high‐ and low‐molecular‐weight glutenins following a modified Verbruggen extraction method. Each fraction was characterized based on the extraction efficiencies within various aqueous alcohols of propan‐1‐ol, electrophoretic patterns, intrinsic and extrinsic fluorescence, free and total sulfhydryl content, and total disulfide bond levels. Findings indicated significant changes to the composition of extracted proteins and modifications to the protein structure (i.e., surface properties and conformation) throughout the ethanol/DDGS process, beginning with the first step of production (PL, ≈83°C). Overall, processing resulted in a shift toward an unextractable gluten matrix, accompanied by increases in hydrophobicity, disulfide bridging, and excessive protein aggregation.
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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".