Cold gelation of canola protein isolate and canola protein hydrolysates
Bibliographic record
Abstract
Canola holds untapped potential as an emerging plant protein source, owing to its promising nutritional and functional advantages. In this study, canola protein isolate (CPI) and its hydrolysates were used to prepare CaCl 2 -induced cold-set gels. Gel properties were examined through rheological characterization and scanning electron microscopy. CPI was extracted from canola meal using an alkaline extraction or salt extraction methods. The gel prepared from alkali-extracted CPI exhibits clearly higher storage and loss moduli values in a frequency sweep test, indicating that exposing hydrophobic domains at alkaline condition is critical for the cold-set gelation. Enzymatic hydrolysis of alkali-extracted CPI using different concentrations of Alcalase (0.04%, 0.2%, and 1%, w/w), resulted in a decrease in gel strength, primarily due to the loss of high molecular weight constituents. Ultrafiltration of the hydrolysates was employed to concentrate these constituents. Interestingly, at reduced protein concentrations, gels prepared using the retentate fraction (the hydrolysate prepared at an Alcalase concentration of 0.04 %) exhibited higher values of both moduli than that of the CPI gels. This suggests that enzymatic hydrolysis of CPI exposes hydrophobic domains, facilitating aggregate formation and improving gel forming capability. Fourier transform infrared spectroscopy analysis shows β-sheets was increased after hydrolysis and the subsequent ultrafiltration process. This study supports a key role of hydrophobic interactions in the formation of cold-set gelation using alkali-extracted CPI and the retentate fraction of canola protein hydrolysate. These findings offer potential for the development of novel applications of canola proteins in food and non-food industries.
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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 | 0.000 |
| 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".