Effect of Flaxseed Flour on Rheological Properties of Wheat Flour Dough and on Bread Characteristics
Bibliographic record
Abstract
Flaxseed (Linum usitatissimum) is an oilseed that is high in omega-3 fatty acid, dietary fiber and lignan. These components are responsible for the health promoting effects of flaxseed. Incorporating flaxseed into foodstuffs such as bread is an approach to increase dietary flaxseed. However, the need for a rapid screen method is needed since a complete baking process may take several hours. Correlating Farinograph parameters with bread characteristics may result in a rapid test that would allow for researchers to identify the proper levels of flaxseed addition to bread without a lengthy baking procedure. Therefore, the effect of 0, 6, 10, and 15% (wt/wt) flaxseed flour (FF) on Farinograph rheological properties of bread flour dough and on bread characteristics were investigated. Results showed that FF significantly (p< 0.05) increased dough water absorption, peak time, and mixing tolerance index, but decreased dough stability. Oven spring was significantly lower for all flaxseed treatments compared to the control. However, the loaf volume of bread made with 6 and 10% FF did not differ significantly (p < 0.05) from the control. The FF concentrations tested did not affect the specific volume of the bread even though the water absorption values were higher for doughs with FF. The breads containing flaxseed had darker crust color and a more yellow crumb color compared to control bread, but the crumb structure was not negatively impacted. Results indicated that Farinograph parameters of bread flour dough could be used as a method for screening FF addition to 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 | 0.001 |
| 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".