Green Extraction of Wheat Phenolic Acids Using Microwave‐Assisted Extraction
Bibliographic record
Abstract
Phenolic acids are important secondary metabolites in wheat, existing in free, conjugated, and bound forms. Traditional extraction methods use organic solvents like ethanol and acetone and are labor-intensive procedures. This study examined the extraction of phenolic acids from wheat using microwave-assisted extraction (MAE) with water as the green extractant. The extraction of phenolic acids was performed on whole grain flour and on wheat kernels. MAE conditions were solvent type (water vs. 80% (v/v) ethanol), temperature (140, 160, 170, and 180°C), and extraction time (2, 5, 10, and 15 min). MAE with 80% (v/v) ethanol effectively extracted phenolic acids directly from wheat kernels, although the amount (0.96 ± 0.03 mg/g DW) was much lower than that obtained from the flour (3.52 ± 0.24 mg/g DW). Water, however, proved to be the most efficient solvent for extracting phenolic compounds (5.41 ± 0.25 mg/g flour DW) compared with 80% (v/v) ethanol (3.52 ± 0.24 mg/g flour DW) at 170°C for 10 min. Kernel extracts extracted with water or 80% (v/v) ethanol at 170°C for 15 min yielded 2.21 ± 0.22 mg/g DW and 0.96 ± 0.03 mg/g DW, respectively. The analysis of the phenolic acids revealed that gallic acid was the most abundant acid, ranging from 1802.56 to 92.02 µg/g DW, depending on the extraction conditions. Overall, an efficient extraction of the phenolic acids, even from wheat kernels, was achieved using MAE with water as the green extractant.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".