Biorefinery process for protein extraction from oriental mustard (Brassica juncea (L.) Czern.) using ethanol stillage
Bibliographic record
Abstract
Large volumes of treated process water are required for protein extraction. Evaporation of this water contributes greatly to the energy consumed in enriching protein products. Thin stillage remaining from ethanol production is available in large volumes and may be suitable for extracting protein rich materials. In this work protein was extracted from ground defatted oriental mustard (Brassica juncea (L.) Czern.) meal using thin stillage. Protein extraction efficiency was studied at pHs between 7.6 and 10.4 and salt concentrations between 3.4 × 10-2 and 1.2 M. The optimum extraction efficiency was pH 10.0 and 1.0 M NaCl. Napin and cruciferin were the most prevalent proteins in the isolate. The isolate exhibited high in vitro digestibility (74.9 ± 0.80%) and lysine content (5.2 ± 0.2 g/100 g of protein). No differences in the efficiency of extraction, SDS-PAGE profile, digestibility, lysine availability, or amino acid composition were observed between protein extracted with thin stillage and that extracted with NaCl solution. The use of thin stillage, in lieu of water, for protein extraction would decrease the energy requirements and waste disposal costs of the protein isolation and biofuel production processes.
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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 | 0.000 |
| 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".