Phenolic and protein contents of differently prepared protein co-precipitates from flaxseed and soybean and antioxidant activity and angiotensin inhibitory activity of their phenolic fractions
Bibliographic record
Abstract
The aim of this study was to determine the phenolic and protein contents of differently prepared protein co-precipitates (Co) from flaxseed and soybean and to determine the antioxidant activity and inhibitory activity towards angiotensin-1-converting enzyme (ACE) of their phenolic constituents. Protein co-precipitates were prepared by alkaline extraction involving mixing flaxseed (F) and soybean (S) powders (Mp) or flaxseed and soybean extracts (Me), followed by isoelectric (Ie) precipitation or Ie and heating (Ie-He). The highest protein contents and yields were obtained with Co-Me/Ie (53.28% and 25.58%, respectively) and Co-Me/Ie-He (46.84% and 19.87%), which also contained relatively high amounts of total phenolics (8.10 and 8.57 mg GAE/g). Co-Me/Ie contained more total bound phenolics (4.33 mg GAE/g) than total free phenolics (3.76 mg GAE/g), while Co-Me/Ie-He co-precipitates were richer in free phenolics (4.82 mg/g). Several free phenolic (FP) and bound phenolic (BP) fractions from Co-Me/Ie (FP-50 °C), Co-Mp/Ie (FP-50 °C and BP-base), Co-Me/Ie-He (FP-50 °C) and Co-Mp/Ie-He (FP-50 °C and BP-base) had the highest antioxidant activity, ranging from 24.7% to 29.6%. ACE inhibitory activity of the BP-acid fraction from Co-Me/Ie and FP-25 °C fraction from Co-Mp/Ie-He were the highest (44.6% and 47.5%). Findings suggest that protein co-precipitates from flaxseed and soybean extracts (Co-Me/Ie and Co-Me/Ie-He) have a high potential for use as protein and phenolic-rich functional ingredients that may benefit the formulation of functional foods and nutraceuticals with antioxidant and antihypertensive properties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".