Technoeconomic Prospects for Commercialization of <i>Brassica</i> (Cruciferous) Plant Proteins
Bibliographic record
Abstract
Abstract Brassica oilseed is the second largest oilseed in the world both in terms of seed and meal production. The nutritional value and functional properties of rapeseed/canola (RSC) protein make it a suitable alternative protein in food applications. However, the meal produced from RSC by the current processing technologies undergoes desolventizer‐toasting that degrades the nutritional and functional quality of the meal, thus making it unsuitable as a feedstock for protein extraction. Several widely used technologies for advancing the commercial production of RSC protein were studied. These technologies generally involve aqueous extraction followed by adsorption or membrane separation methods, including (1) the alkali extraction of protein and recovery at low pH, (2) protein micelle formation method, (3) chromatographic separation, and (4) meal component fractionation method. This paper reviews challenges in the current Brassica oilseed protein value chain related to the development and commercialization of RSC proteins in a market dominated by soybean protein. This work also includes an empirical case study of the recent RSC commercialization ventures. Opportunities for the commercialization of oilseed protein in the market are also presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".