Effect of processing on oat β-glucan viscosity, postprandial glycemic response and subjective measures of appetite
Bibliographic record
Abstract
Oat has procured its acclaim as a health promoting food partially due to its positive effect on glucose control. It has been demonstrated that oat β-glucan can interfere with postprandial glucose response. A large majority of this action is attributed to the increase in viscosity due to the β-glucan content in oat foods. While it is known that an increase in viscosity due to higher molecular weight of β-glucan can improve its glycemic effects, it is not known if an increase in viscosity attained by processing variables can further enhance the positive effect of oat on glucose control. In the current study we have examined the effect of kilning, tempering, microwaving, cooking, soaking and flaking on oat β-glucan viscosity. An acute randomized crossover clinical trial was also conducted to test oatmeal products containing low, medium and high β-glucan viscosity for their effect on postprandial glycemic response. Results from the processing experiments demonstrate that kilned samples, when tempered to 25% moisture and microwaved for 2 minutes, can produce much higher final viscosity compared to other samples with similar β-glucan content, molecular weight and solubility. However, results from the clinical trial show that the increase in the viscosity of the oat β-glucan attained through processing in this study did not have any effect on postprandial glucose control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".