Enhancing the breadmaking quality of ancient grains through synergistic emulsifier treatment and extrusion processing
Bibliographic record
Abstract
The objective of this study was to improve the overall breadmaking characteristics of ancient wheat species through emulsifier treatment, extrusion processing, and by the synergistic effect of these two approaches. Ancient wheat flours were treated with four emulsifiers-distilled monoglycerides (DMG), diacetyl tartaric acid ester of monoglycerides (DATEM), sodium stearoyl lactylate (SSL), and defatted soy lecithin-at three different concentrations. Extrusion was conducted at three different screw speeds (100, 150, and 200 rpm) with a constant moisture content of 30%. Following these different treatments, pasting, mixing, and thermal properties of the flour samples were examined. Concomitantly, whole grain breadmaking was conducted by combining the treated flours with base flour. SSL at 0.45% increased pasting viscosities and enhanced dough handling properties in einkorn and spelt flour samples. Extrusion, in general, significantly (p < 0.05) increased water absorption value by inducing starch damage. Extrusion impacted the dough stability and dough development time negatively with increasing intensity. The increase in extrusion intensity also negatively affected the pasting parameters of extruded flour by complete gelatinization and loss of crystallinity. The water absorption value of extrudate-added bread dough increased significantly (p < 0.05). However, emulsifier treatment or extrudate-added flour did not improve breadmaking attributes of emmer bread. The overall volumes of emulsifier-treated (50%) einkorn and emmer breads were higher than that of base flour bread. Overall, our findings indicated that optimizing the combination of emulsifier treatment and extrusion can potentially improve dough handling and breadmaking properties of ancient wheat species.
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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) | 0.000 | 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".