Solvent and Extraction Conditions Control the Assayable Phenolic Content and Antioxidant Activities of Seeds of Black Beans, Canola and Millet
Bibliographic record
Abstract
Abstract The effects of extraction solvent and conditions on the total phenolic content (TPC) and antioxidant activity of black beans, canola and foxtail millet were investigated. The antioxidant activity was assayed using 2,2‐diphenyl‐1‐picrylhydrazyl radical scavenging activity (DRSA) and oxygen radical absorbance capacity (ORAC). Four solvent systems, namely 70 % acetone, 80 % ethanol, 80 % methanol and a mixture of acetone/methanol/water (7:7:6, v/v/v) were used. The extraction methods adopted in this study included refluxing, homogenization, cold extraction and sonication. The TPC as measured using the Folin Ciocalteu's method were 12.35–28.39, 2.43–16.73, and 1.78–5.06 µmol catechin equivalents/g dry matter (dm) for canola, black beans and foxtail millet, respectively. Aqueous acetone afforded the highest TPC for black beans and canola. Within the same solvent system used, the TPC, DRSA and ORAC obtained from different extraction techniques differed for black beans, canola and foxtail millet. The results demonstrated that the solvent system as well as method influenced the extraction of phenolic compounds and their antioxidant activities, depending on the type of matrix in which phenolics were embedded.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".