Impact of pH on the extraction of different mustard seeds and their applications
Bibliographic record
Abstract
Canada is one of the world’s top producers of mustard, and according to the Canadian Grain Commission, its yield varied between 50,000-286,700 tonnes over the last twenty years. Besides being high in protein and oil, mustard is rich in phenolic compounds with great potential for application in food production, nutraceuticals, and cosmetics. The most noticeable compounds that are found in mustard are sinapine, sinapic acid and its conversion to canolol. However, the production of mustard has not fully exploited this oilseed and its functional potential, especially its bioactivities. Therefore, a more effective extraction method has been researched. In this study, a home-scale system with the application of temperatures, pressure and pH was designed to optimize the extraction of the sinapine, and sinapic acid for the generation of canolol from Oriental, black and yellow mustard varieties. Experiments proceeded with whole and crushed seeds using sautéing as a preheating treatment, followed by acidified-, neutralized- and alkaline-pressurized wet extraction. HPLC analysis, different antioxidant assays together with total phenolic content (TPC) and total flavonoid content (TFC) as long as the anti-tyrosinase activity was used for the quantification of extraction efficiency. This extraction system proved to be productive, with the highest targeted major sinapates obtained from yellow and black mustard. Moreover, the strongest antioxidant and anti-tyrosinase activity was also observed for both yellow and black mustard extracts providing in-vitro evidence for the potential application of mustard for nutraceuticals and in cosmetic production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".