Monoacylglycerol gel offers improved lipid profiles in high and low moisture baked products but does not influence postprandial lipid and glucose responses
Bibliographic record
Abstract
Structured emulsions, including monoacylglycerol (MAG) gels, are of interest as alternatives to shortenings rich in saturated and trans fatty acids (SFA and TFA). However, an understanding of their physical and nutritional functionality in baked products is limited. The objective of this randomized crossover study was to compare the postprandial lipid and glucose responses to two different baked product matrices produced with a MAG gel. Differences between study treatments are discussed in the context of underlying ingredient interactions impacting, primarily, starch digestibility. Healthy males (n = 18, 19-40 years, BMI ≤27 kg m(-2), waist circumference ≤102 cm, fasting plasma glucose <5.6 mmoL L(-1), insulin <180 pmol L(-1) and TAG <1.7 mmol L(-1)) attended six study visits, each separated by at least one week, and consumed one of six study treatments with subsequent blood sampling for 6 h for determination of triacylglycerol (TAG), glucose, insulin and free fatty acids (FFA). The study treatments consisted of sugar-free cakes and cookies (high and low moisture products, respectively) produced using either the canola oil-based structured MAG gel or the compositionally-equivalent MAG gel ingredients. Although MAG gel structure per se did not impact postprandial response, all cookies had higher TAG responses compared with cakes, even when matched for fat content. Sugar cookies containing 40 g of the MAG gel or an industry standard stearic-rich shortening were also compared, with no differences observed in postprandial response.
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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) | 0.002 | 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".