The effect of pH on the phenolic content and antioxidant properties of three different mustard extracts
Bibliographic record
Abstract
Abstract Mustard seeds are cultivated worldwide due to their substantial agronomic value of their high protein, oil, and phenolic content. The latter bioactive compounds give mustard seeds various applications in the food and pharmaceutical industries, as antimicrobial, antioxidant, and chemoprotective agents. By modifying the pretreatment and extraction conditions, a significant improvement in the quantity and quality of these crucial compounds was obtained. Based on the electrostatic interactions between the solvents and the extracts, an alternative green extraction procedure was used on three varieties of mustard seeds (Oriental, black, and yellow). Preliminary results demonstrated an interesting trend in which the isoelectric pH value affected the antioxidant activity of the extracts. A number of different antioxidant assays together with total phenolic content (TPC) and total flavonoid content (TFC) were conducted on the three different mustard seeds as affected by different combinations of times and pHs. With the exception of metal ion chelation assay, the other antioxidant methods, including ferric reducing/antioxidant power assay, 2,2‐diphenyl‐1‐picrylhydrazyl free radical‐scavenging assay and ABTS •+ scavenging assay, significantly ( p < 0.05) increased with the pretreatment time for all three pH levels studied. Interestingly, the TPC significantly increased ( p < 0.05) with the lower pH level treatments. The highest TPC (2040.32 ± 360.12 mg/g dry weight basis) was obtained from yellow mustard seed under neutral treatment. Conversely, TFC showed no significant differences among the different pretreatment time conditions closer to the neutral pH. Practical Application The usage of food‐based solvents with the assistance of a home‐scale pressurized wet extraction model represents a green technology that can contribute to a wide variety of applications. This method significantly improved the phenolic content, flavonoid content, and antioxidant potential of the mustard extracts, thus making water the most promising extracted solvent.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".