Oil‐free protein isolates from full‐fat, dehulled mustard flour by microfiltration
Bibliographic record
Abstract
Abstract New microfiltration (MF)‐based aqueous processes for the extraction of oil‐free protein isolates from full‐fat, dehulled mustard flour have been developed. The processes used hydrophilic MF membranes to separate oil and protein. The most successful processing sequence consisted of alkaline extraction of oil and protein from the flour at pH 11, centrifugation to remove undissolved solids, and an initial microfiltration step to separate oil and dissolved protein. This was followed by proteolytic enzyme treatment of the retentate and a second‐stage MF step to recover further protein. The proteolytic enzyme treatment was designed to break aggregated proteins into smaller fractions to permit their passage through the MF membrane. The permeates from the two microfiltration steps were ultrafiltered to concentrate the protein and remove antinutritional compounds and highly fragmented peptides. With this process, 60% of the protein originally in the flour was separated from the oil phase. Approximately 40% of the protein present in the flour was recovered in the form of two oil‐free protein isolates—a soluble protein isolate containing 91% protein and a precipitated protein isolate containing 100% protein. The process proves the potential of MF as an effective method of extracting oil‐free protein isolates from oilseeds without the use of organic solvents.
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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 | 0.000 | 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) | 0.000 | 0.000 |
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".