Effects of natural deep eutectic solvents’ hydration level, choice of hydrogen bond donor and application of ultrasound on the extraction, anti-nutritional components, structural properties and functionality of canola protein isolates
Bibliographic record
Abstract
In a systematic attempt to improve the cost-effectiveness and knowledge of the topic of plant protein extraction using natural deep eutectic solvents (NDESs), 18 extraction treatments, half of which paired with sonication, were performed to extract canola protein isolates using two groups of NDESs: choline chloride-glucose-water (CG) and choline chloride-water (C), with water contents ranging from 30 to 90 %. The highest protein extraction efficiencies were observed at water contents of 60 % for CG group (50.28 %) and 90 % for C group (45.48 %), while sonication improved these efficiencies to 60.68 % and 58.11 %, respectively. Increased solvent hydration and sonication also effectively reduced phenolic compounds and phytic acid contents, especially in the CG group. Despite increasing the solvents' viscosity and density, the combination of glucose and water as hydrogen bond donors proved more effective for protein extraction than water alone, especially for larger fractions such as cruciferins and aggregates. Furthermore, while protein secondary structure remained mostly intact, variations in solvents' water content, and sonication, affected the tertiary structure and particle size distribution, with the strength and flexibility of the NDESs' nanostructure possibly affecting the protein conformation and aggregation. Regarding functionality, sonicated isolates showed an average of 8.31 % lower aqueous solubility across the pH range of 3-7, along with more than double the emulsion stability and a lower foam stability at pH 3, compared to non-sonicated isolates. Overall, pushing NDESs to their upper hydration limit, proper selection of hydrogen bond donors, and the application of ultrasound can improve the cost-effectiveness and quality of extracted canola protein isolates.
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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 | 0.001 |
| 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 | 0.001 |
| 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".