Deep eutectic system enhanced oat protein extraction
Bibliographic record
Abstract
Oats are a rich source of plant-based proteins owing to their nutritional value, diverse functions, and high abundance. However, traditional methods for extracting oat proteins (OPs), such as alkali solution acid precipitation (ASAP), can cause environmental pollution and potentially protein denaturation. In this work, we studied the use of deep eutectic solvents (DESs) and deep eutectic system (DESys)-based methods for OP extraction. The DES are composed of ionic liquids (ILs) and choline chloride (ChCl) as hydrogen bond acceptors (HBAs), and polyols as hydrogen bond donors (HBDs) for OP extraction. By systematically investigating the extraction conditions, it was found that using ChCl as an HBA in the DESys-based method allowed for a significant increase in protein recovery yield compared to the ASAP and DES-based methods. Furthermore, the physicochemical properties of OPs extracted using the ASAP, DES, and DESys-based methods exhibited some differences, particularly in their molecular structure, amino acid composition, and thermal properties, suggesting that the properties of OP could be potentially adjusted by DESys- and DES-based methods. When considering both toxicity and protein recovery yield, the DESys-based extraction method using ChCl as the HBA is more suitable for OP extraction. This study demonstrated a green and efficient method for OP extraction that minimizes environmental impact, potentially bridging the gap between ILs and DES, and offering insights for designing new DES- or DESys-based extraction strategies for biological molecules.
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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 | 0.001 |
| 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) | 0.000 | 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".