Comparison of Oil, Shortening, and a Structured Shortening on Wheat Dough Rheology and Starch Pasting Properties
Bibliographic record
Abstract
ABSTRACT Monoacylglycerol‐stabilized oil in water emulsion (MAG gel) is an alternate shortening that is free of trans fatty acids, and low in saturated fatty acids. However, the behavior of MAG gels in comparison to other lipids has not been studied. This study investigated effects of structured MAG gel, a mixture of MAG gel unstructured components (Mixture), canola oil (Oil), or interesterified soy shortening (IE Soy) at different levels (6–24%) on hard or soft wheat dough properties. Doughs were prepared with different lipid types at equivalent lipid contents. Dough mixing and water absorption parameters were evaluated using a farinograph; gluten behavior was measured using a gluten peak tester (GPT); and pasting characteristics were measured using a micro‐viscoamylograph (MVAG). Water absorption values decreased with increasing lipid content. Dough development times were similar between the MAG gel and IE Soy, but farinogram curve characteristics during mixing were similar between MAG gel, Mixture, and Oil. The trend for peak max time in GPT was similar between MAG gel and IE Soy exhibiting delayed gluten aggregation; whereas Mixture and Oil exhibited earlier gluten aggregation. In MVAG, starch interaction with monoglyceride component of MAG gel and Mixture appeared to be the dominating factor resulting in increased pasting temperature and a second viscosity peak during cooling at higher levels of lipid addition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".