Identification and Antioxidant Properties of Phenolic Compounds during Production of Bread from Purple Wheat Grains
Bibliographic record
Abstract
Phenolic profiles and antioxidant properties of purple wheat varieties were investigated to document the effects of bread-making. Bread crust and crumb along with samples collected after mixing, 30 min fermenting, 65 min fermenting, and baking were examined. Free phenolic content (105.4 to 113.2 mg FAE/100 g) significantly (p < 0.05) increased during mixing, fermenting, and baking (65% to 68%). Bound phenolics slightly (p > 0.05) decreased after 30 min fermentation (7% to 9%) compared to the dough after mixing, but increased significantly (p < 0.05) during 65 min fermenting and baking (16% to 27%). Their antioxidant activities followed a similar trend as observed for total phenolic content. The bread crust demonstrated increased free (103% to 109%) but decreased bound (2% to 3%) phenolic content, whereas bread crumb exhibited a reversal of these results. Total anthocyanin content (TAC) significantly (p < 0.05) decreased by 21% after mixing; however, it gradually increased to 90% of the original levels after fermenting. Baking significantly (p < 0.05) decreased TAC by 55%, resulting in the lowest value for bread crust (0.8 to 4.4 mg cyn-3-glu equiv./100 g). p-Hydroxybenzoic, vanillic, p-coumaric, and ferulic acids were detected in free-phenolic extracts, while protocatechuic, caffeic syringic, and sinapic were additional acids in bound-phenolic extracts. Cyanidin-3-glucoside was the detectable anthocyanin in purple wheat. Bread-making significantly (p < 0.05) increased the phenolic content and antioxidant activities; however, it compromised the anthocyanin content of purple wheat bread.
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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".