Effect of canola meal fermentation and protein extraction method on the functional properties of resulting protein products
Bibliographic record
Abstract
Abstract The present study investigated the effect of solid‐state fermentation (SSF) of cold‐pressed (CP) and hexane‐extracted (HE) canola meals with Aspergillus niger NRRL 334 and Aspergillus oryzae NRRL 5590 on the functionalities of protein products extracted from them. After SSF, proteins were recovered using alkaline extraction‐isoelectric precipitation (AE‐IP) or salt extraction‐dialysis (SE). SSF of the two meal types reduced the protein content of the extracts produced by AE‐IP. There were varied effects to solubility, foaming, and emulsifying properties as a result of SSF under the combined influence of functionality pH, strain, meal type, and protein extraction method. The protein isolate produced from CP meal using SE had increased solubility at pH 7 (from 51.8% to 90.7%) when the meal was fermented with A. oryzae. Both strains resulted in an over twofold increase in the emulsifying activity index (at pH 7) of AE‐IP products from CP meal. For both protein extraction methods, the protein products from A. niger fermented HE meal had better foaming capacity (FC) at pH 7 than the controls (non‐fermented), but reduced FC at pH 3. Overall, regardless of meal fermentation, the SE products were richer in protein and had higher oil holding capacity (OHC), whereas the water holding capacity (WHC) was higher for AE‐IP isolates. SSF of the meals generally improved the O/WHC of the extracted proteins. The findings suggest that canola protein functionality could be effectively modulated by SSF with different microbial strains under various processing conditions to enhance their applicability in the food industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".