Extrusion of Oilseed‐Based Ingredients: Unlocking New Potential for Sustainable Protein Solutions
Bibliographic record
Abstract
The growing demand for plant-based proteins has driven significant interest in utilization of oilseed cakes and meals, which are abundant byproducts of the oil extraction industry. These protein-rich products possess unique functional properties that make them valuable for various food applications in a sustainable and cost-effective way. This review provides an in-depth review of extrusion processes as tools to enhance the functionality of oilseed cakes, meals, and proteins. Under specific processing conditions that dictate thermal and mechanical energy input, extrusion induces structural and functional modifications in proteins, which, in turn, improves the digestibility, reduces antinutritional factors, and enhances the overall nutritional profile of oilseed cakes, meals, and proteins. The importance of optimizing key extrusion parameters and the role of residual oil content in the process are discussed. Additionally, the diverse applications of extruded oilseed proteins in developing meat alternatives, snack foods, and breakfast cereals are highlighted. Advanced techniques such as fermentation and enzyme hydrolysis as treatments prior to extrusion are also examined for their potential to further improve the sensory and nutritional properties of extruded products. Relevant literature published between 2000 and 2024 was identified using databases such as Scopus and Web of Science, with keywords including oilseed proteins, extrusion, and plant-based meat alternatives. Studies were selected based on relevance to processing techniques, functional outcomes, and food applications. This comprehensive analysis underscores the potential of extrusion technology to unlock new opportunities for oilseed cakes and their protein-rich fractions in the food industry, contributing to the development of innovative, plant-based food products that meet consumer demands for nutrition and sustainability.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".