Pearl millet (<i>Pennisetum glaucum</i>) couscous breaks down faster than wheat couscous in the Human Gastric Simulator, though has slower starch hydrolysis
Bibliographic record
Abstract
Consumption of traditional West African pearl millet (Pennisetum glaucum) couscous delayed gastric emptying in our recent human study compared to other starch-based foods (white rice, boiled potatoes, pasta). The objective of this study was to determine whether physical properties of pearl millet couscous affect particle breakdown and starch hydrolysis during simulated gastric digestion to understand the basis of the slow gastric emptying. Starch fine structure and viscosity were analyzed for initial millet and wheat couscous samples by high performance size-exclusion chromatography and the Rapid Visco Analyzer, respectively. Couscous samples were subjected to simulated gastric digestion using the Human Gastric Simulator (HGS), a dynamic model of human gastric digestion. Digesta was collected from the HGS at 30 min intervals over 180 min. Particle size and percent starch hydrolysis of couscous in the digesta were evaluated at each time point. The number of particles per gram of dry mass substantially increased over digestion time for millet couscous (p < 0.05), while changed little for the wheat couscous samples. Millet couscous showed lower starch hydrolysis per unit surface area of particles than wheat couscous (p < 0.05). Slower starch hydrolysis was associated with smaller (p < 0.05) amylose chain length for millet (839-963 DP) than for wheat (1225-1563 DP), which may enable enable a denser packing of millet starch molecules that impedes hydrolysis. We hypothesize that the slow gastric emptying rate of millet couscous observed in humans may be explained by its slow starch hydrolysis property that could activate the ileal brake system, independent of high particle breakdown rate in the stomach.
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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) | 0.001 | 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".