RECOVERY OF SINAPIC ACID FROM A WASTE STREAM IN THE PROCESSING OF YELLOW MUSTARD PROTEIN ISOLATE
Bibliographic record
Abstract
ABSTRACT A large amount of waste permeates generated from the processing of yellow mustard protein was concentrated fivefold using a nanofilter with a molecular weight cut off of 1,000 Da, while approximately 74% of sinapic acid was retained. Sinapic acid was then released from sinapine, its esterified form, by an alkaline hydrolysis. The hydrolyzed solution was acidified to prevent oxidation of the sinapic acid and to precipitate the remaining proteins. Subsequently, sinapic acid and other phenolics were extracted by a two‐stage extraction using a mixture of diethyl ether and ethyl acetate (1:1), 165‐min extraction time and permeate‐to‐solvent ratio of 1:2. Approximately 95% of the sinapic acid in the acidified permeate was finally concentrated in the solvent phase. PRACTICAL APPLICATIONS This development has led to an economical process to recover phenolics and to treat effluent from a process of oilseed protein while reducing the use of water during the extraction of protein. A reduction of water usage makes the processing of oilseed protein isolate more economically attractive, and the recovered phenolics may find a use as a nutraceutical. The developed process is not only limited to the recovery of phenolics from mustard, but also applied for recovering phenolic acids, specifically sinapic acid, from waste water from membrane processing of protein from mustard and similar polyphenol‐containing oilseeds.
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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) | 0.000 | 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) | 0.001 | 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".