Effects of Monoglycerides of Varying Fatty Acid Chain Length and Mixtures Thereof on Sponge‐and‐Dough Breadmaking Quality
Bibliographic record
Abstract
Monoglycerides are widely used in the baking industry because of their antistaling effects, mainly suppressing crumb firming. Commercial monoglycerides are normally prepared from hydrogenated fats, with stearate being the most common fatty acid. In a previous study, monoglycerides such as monopalmitate (C16) and monostearate (C18) had positive effects on Canadian short process bread but no improvements on sponge‐and‐dough process (SDP) bread. The objective of this study was to investigate the effects of saturated monoglycerides of varying fatty acid chain length (C14–C22) on SDP breadmaking quality by using volume judgment, crumb image analysis, and texture measurements. Higher levels (1.00–1.50%) of all monoglycerides (C14, C16, and C18) significantly (P < 0.05) increased loaf volume and cell diameter. The larger cell diameter with increasing levels of these monoglycerides may have resulted from softer, more extensible dough handling properties and greater gas cell stability during baking. Addition of C16 and C18 caused the largest increase in crumb softness with increasing monoglyceride levels but showed relatively low resilience, which might be related to larger loaf volume (i.e., lower density of bread). However, addition of blended monoglycerides C14+C16 increased crumb softness and loaf volume while partially retaining resilience. Each monoglyceride had a different function in breadmaking quality and somewhat positive effects on SDP.
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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 | 0.001 |
| 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".