Bibliographic record
Abstract
Whole grain intake may be linked to a lowered risk of chronic diseases and such positive effects might be attributed to phenolic lipids, alkylresorcinols (ARs), found in cereal bran.This study aimed to characterize ARs in wheat bran (WB), investigate the influence of environmental factors on AR composition, measure AR bioactivity including antioxidant activity (AA) and investigate AR stability during baking.Moreover, the prebiotic potential of WB-soluble dietary fibre (SDF) was explored using yogurt models.Specific objectives were achieved by the following projects.The effects of cultivar, and region on the ARs content in 24 wheat cultivars grown in Ontario were studied, by GC-MS and their AA were evaluated.TPC (3.0 to 58.0 mg FAE/g), DPPH (5 to 68%), ORAC (6.0 to 94.0 µmol TE/g) and ARs (21.0 to 1522.0 μg/g) of WB extracts were significantly affected by location and cultivar (P < 0.05).The stability of WB-ARs was studied using different bread formulations.Bread ARs (1.1 to 82.9 mg/100 g) were heat stable during baking at 255 °C for 12 min.A positive correlation was observed between TPC and ORAC (R 2 = 0.90).Two extraction methods were used to compare the AR contents of WB.The % ARs per extract using acetone versus SC-CO 2 extraction of both WB samples studied were in the range of 10.9% -15.6% and 5.1% -6.6%, respectively.The prebiotic potential of WB-SDF to enhance lactic acid bacteria (LAB) survival in yogurt models was explored.Total DF (53%) was counted as a sum of SDF (6%) and IDF (47%).HPLC analysis of DF fractions treated with alkaline hydrolysis showed that IDF (84.2%) had a higher phenolic acid (PA) content than SDF (15.8%).There was a significant difference in total bacterial count (9.1 log CFU/mL), pH (4.8) and TTA iii (1.4%) in yogurt samples containing 4% WB compared to controls during the four week cold storage period (4 °C).These results could lead to practical applications of WB-ARs and SDF in the functional foods and nutraceutical industry.Furthermore, increased WB utilization will reduce Agri-Food leftovers, thus improving environmental sustainability.honored to be her first graduate student in her academic career in 2009.From the day I met her, she has always been encouraging, enthusiastic, and hardworking about her research.Her passion for science, teaching and research
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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) | 0.000 | 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) | 0.001 | 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".