Sustainable Biosynthesis of Antioxidants from Koji Rice Fermented with Aspergillus flavus Using Microwave-Assisted Extraction
Bibliographic record
Abstract
The present study proposes microwave-assisted extraction as a sustainable technique for the biosynthesis of bioactive compounds from rice fermented with Aspergillus flavus (koji). First, fermentation conditions (i.e., pH from 3–12, five temperatures from 20–40 °C, and four culture-fermentation media viz. wheat, wheat bran, malt and rice) were optimized for producing microbial bioactive compounds. Microwave extraction was performed at 2450 MHz and 500 W for 20, 30, and 40 s with seven solvents (distilled water, ethyl acetate, hexane, ethanol, chloroform, diethyl ether, and methanol). The obtained results revealed that ethyl acetate is the most appropriate solvent for extraction. Effects of this ethyl acetate extract were compared with a commercial synthetic antioxidant. Antioxidant properties were enhanced by preventing the oxidation of the linoleic acid (C18H32O2) with an inhibition rate (antioxidant efficacy) of 73.13%. Notably, the ferrous ion binding ability was marginally lower when compared to the disodium salt of ethylenediaminetetraacetic acid (EDTA). Additionally, the obtained total content of phenolic compounds in the ethyl acetate extract of fermented rice (koji) by Aspergillus flavus was 232.11 mg based on gallic acid/mL. Antioxidant compounds in the ethyl acetate extract of fermented rice showed stability under neutral conditions, as well as at high temperatures reaching 185 °C during 2 h, but were unstable under acidic and alkaline conditions. The results demonstrate the efficacy of novel microwave-assisted extraction technique for accelerating antioxidant production during rice fermentation.
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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".