Polypeptide composition of major oilseed proteins and functional properties of extracted protein products: A concise review
Bibliographic record
Abstract
Abstract Oilseeds are grown mainly for their oil content but the residues (meals) that remain after defatting are excellent sources of plant protein ingredients. However, to serve as useful ingredients, the extracted proteins must meet industry expectations in terms of functional performance. Protein functionality is influenced by structural conformation, amino acid composition, type of polypeptides, presence of non‐protein materials (carbohydrates, lipids, and polyphenols), which in turn can be modified by the extraction method. Defatted oilseed meals are extracted mostly through the pH shift method, which involves alkaline solubilization followed by acid‐induced protein precipitation at the isoelectric point. A less popular method is called the protein micellar mass whereby the oilseed meal proteins are extracted with a NaCl solution, which is later diluted to reduce the ionic strength to a level where the proteins are no longer soluble and hence precipitate. A third method utilizes carbohydrases and phytases to first digest non‐protein materials from the oilseed meal into smaller units that are then removed by membrane ultrafiltration to leave behind a protein‐rich extract. These methods produce mainly two types of isolated oilseed proteins, concentrates (60%–89% protein content) and isolates (≥90% protein content), which can differ in terms of their protein conformation, solubility, and functionality as food ingredients. Therefore, this review provides an overview of the extraction and isolation as well as structural and functional properties of soybean, peanut, canola, hemp seed, sunflower, and sesame seed proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".