Effects of solid-state fermentation on the phytochemical composition and antioxidant activities of oriental mustard ( <i>Brassica juncea</i>) and yellow mustard ( <i>Sinapis alba</i>) bran
Bibliographic record
Abstract
Mustard bran is enriched with bioactive phenolic compounds and glucosinolates, yet it is underutilized as a low-value processing by-product. Here, we investigate the effects of solid-state fermentation (SSF) using various food-grade microorganisms (Aspergillus spp., Rhizopus spp., Bacillus subtilis, Saccharomyces cerevisiae) on the phytochemical composition and antioxidant activities of oriental mustard and yellow mustard brans. The total phenolic contents (TPC) and antioxidant activities (FRAP, DPPH assays) of oriental and yellow mustard brans were significantly improved (p < 0.05) after fermentation, especially by R. oligosporus and R. oryzae. Moreover, SSF by R. oligosporus and R. oryzae significantly increased (p < 0.05) the levels of p-hydroxybenzoic acid, syringic acid, protocatechuic acid, sinapic acid and kaempferol-3-O-glucoside in both mustard brans. Conversely, a significant reduction (p < 0.05) of major glucosinolates in oriental and yellow mustard brans were observed after SSF by R. oligosporus. Findings from this study show that SSF by filamentous fungi is a promising strategy to enhance the phenolic contents, antioxidant properties and overall value of oriental and yellow mustard brans.
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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".