Isolation and characterization of proteins from defatted flaxseed meal
Bibliographic record
Abstract
Interest in flaxseed and products derived from flaxseed has increased considerably in the past decade. In the development of nutraceutical foods, several flaxseed components including lignans and alpha-linolenic acid, have been recognized to have health benefits. There are many patents and health claims to these components; however, relatively little research and information is found on flaxseed proteins. The objective of the present study was to isolate and characterize flaxseed proteins from defatted flaxseed meal. Proteins were extracted from defatted flaxseed meal with NaOH. NaCl and NaCl/Papain. Protein solubility with these extractants ranged from 18 to 25%. Proteins were precipitated from the extracts using one or more of the following precipitation techniques; isoelectric precipitation (IP) gave yields and protein contents ranging between 22--25% and 67--73% respectively. Co-precipitation with soy and whey proteins gave yields of 26.95 and 35.78% respectively for a NaOH extract. Chemical hydrolysis of flaxseed meal with NaOH, NaCl and NaCl/Papain extraction and IP increased protein solubilization (35--43%) and protein yields (19--37%). Proteins in the extracts and isolates were characterized by polyacrylamide gel electrophoresis (PAGE). Proteins extracted with NaOH and NaCl gave two bands by native-PAGE with molecular weights (MW) of 320 and 514KDa; proteins extracted with NaCl/Papain gave a single band at 188KDa. SDS-PAGE of isolates extracted with NaOH, NaCl and NaCl/Papain gave subunits with MW ranging from 6.5 to 56KDa. The major fractions isolated from NaOH-IP by RP-HPLC showed subunits with MW ranging from 6.5 to 40.1KDa by SDS-PAGE and 5.9 to 42.5KDa by ESI-MS. Subunits characterized by ESI-MS have not been reported previously in the literature.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".