Canola Protein: A Promising Protein Source for Delivery, Adhesive, and Material Applications
Bibliographic record
Abstract
Abstract Canola is second only to soy with regard to production volume, but the meal after extraction has limited value‐added applications, apart from its use as feed. Emerging interest in the meal is to develop nonfood applications such as delivery systems, adhesives, or plastics. This review critically evaluates the recent progress in research on the applications of value‐added canola protein, especially on nonfood applications such as plastics, films, packaging materials, adhesives, and drug delivery applications, and presents the perspectives on the future directions of canola protein utilization. Canola protein with suitable surface activity, gelation, interaction with other polymers, gastric and heat resistance, and biodegradability has the ability to form carriers to encapsulate, protect, and deliver bioactives/drugs. Canola‐based adhesives prepared by denaturation, chemical modification, cross‐linking, blending with synthetic resins, and nanomaterial addition show promising results in applications as adhesives. Canola protein‐based plastic films are mostly prepared by solution casting with the aid of plasticizers to improve the ductility of the protein network through increasing the mobility of protein chains; incorporation of cross‐linking agents could also increase protein–protein interactions toward the formation of stronger films. Canola proteins show wide potential for use as an encapsulant in delivery systems, as an adhesive, and as a plastic; however, further research is needed to improve the performance and to make it cost effective.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".