Effect of pH and defatting on the functional attributes of safflower, sunflower, canola, and hemp protein concentrates
Bibliographic record
Abstract
Abstract Background and objectives The effect of defatting four oilseed protein concentrates (safflower, sunflower, canola, and hemp) on the surface and functional properties of the proteins was investigated as a function of pH (pH 3, 5, 7). The functionality of commercial protein concentrates (soy, faba bean, lentil, pea, northern great bean, whey) already in the marketplace was also tested for comparative purposes. Findings Defatting with hexane increased the protein content from 77.3% to 92.9% for safflower, 67.5 to 75.5% for sunflower, 58.0–66.0% for canola, and 71.0–83.2% for hemp. The approximate isoelectric point (pI) of safflower increased with defatting (5.4–5.8), whereas for canola the pI decreased with defatting (4.7–4.3), and sunflower and hemp protein concentrates had similar pI for defatted or full fat. Certain functional properties were improved with defatting, whereas others showed the opposite trend; this was highly dependent on protein type and pH. Conclusions The oilseed concentrates were comparable to the concentrates in the marketplace with canola and sunflower proteins having the greatest oil‐holding capacity and defatted safflower having the highest foaming capacity of all the proteins tested. Significance and novelty Based on their functionality, the oilseed protein concentrates have potential to be used by the food ingredient 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 | 0.001 | 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 | 0.000 |
| 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".