Canola meal valorization via acid hydrolysis to generate free amino acids
Bibliographic record
Abstract
Abstract This study investigated an alternative approach to valorizing canola proteins by hydrolyzing them to generate amino acids (AAs). Pre‐treatment of cold‐pressed (CP) cake and desolventized‐toasted (DT) meal with ethanol (99%, vol/vol) followed by protein separation was studied as process optimizations to maximize protein recovery with higher purity. The optimum ethanol pre‐treatment conditions to achieve a meal containing less than 1% oil was reached at a meal‐to‐ethanol ratio of 1:4 (wt:wt) and 50°C for 30 min extraction. The protein recovery reached the maximum at pH 12 and a meal‐to‐solvent ratio of 1:10 (wt:vol), yielding 73% and 33% recovery from ethanol pre‐treated CP and DT meals, respectively, in a single extraction. Untreated and ethanol pre‐treated meals were hydrolyzed with 6 N HCl (protein‐to‐acid ratio of 5 mg:2 mL) for 24 h at 110°C. The ethanol pre‐treatment improved AA recovery and released 373 mg AA/g dry CP meal biomass (dbm) compared to 279 mg AA/g untreated CP cake dbm. However, no improvement in AA recovery upon ethanol pre‐treatment of DT meal. Sulfuric acid was examined as an alternative acid. More than 700 mg AA/g CP protein were released with 6 N H2SO4, while for DT meal proteins, a 10 N concentration was needed to achieve a closer value. Commercial canola meals can be utilized for generating free AAs; however, the meal processing history may affect the yield.
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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) | 0.001 | 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".