Functional Attributes of Proteins Withdrawn from Different Stages of a Commercial Ethanol Fuel/Distillers Dried Grains with Solubles Process Using a Wheat Feedstock
Bibliographic record
Abstract
ABSTRACT The functional attributes of proteins withdrawn from different stages of a commercial ethanol fuel/distillers dried grains with solubles (DDGS) process that used a wheat feedstock were investigated. Specifically, protein coproducts were examined postliquefaction (PL), postdistillation (PD), and postdrying (DDGS) and related to those from the native wheat feedstock (WF). Water hydration, oil holding, foaming, and emulsifying properties of all materials were assessed. The WF and the PL coproduct showed similar water hydration capacities (WHCs), ranging between 80 and 110%, whereas WHCs for the PD and DDGS coproducts were significantly higher (ranging between 264 and 356%). WHC for all products was found to be independent over the pH range of 2.00–10.00. A similar trend was found for the oil holding capacities, for which both WF and PL materials showed lower values (≈120%) than both the PD (203%) and DDGS (171%) materials. Foam capacity and stability for the WF were found to be 32.8 and 61.4%, respectively, whereas no coproducts were foam forming. Emulsion stability decreased from ≈45% for the WF to <10% in all other materials. Findings indicated that initial changes to the protein's functional attributes occurred in the PL stage of ethanol/DDGS processing.
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 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 | 0.001 |
| 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".