In Vitro Starch Digestibility and Expected Glycemic Index of Kodo Millet (<i>Paspalum scrobiculatum</i>) as Affected by Starch–Protein–Lipid Interactions
Bibliographic record
Abstract
ABSTRACT The effect of starch–protein–lipid interaction on the in vitro starch digestibility and expected glycemic index (eGI) of kodo millet flour (MF) was investigated. Debranned MF and the flour with lipid removed, protein removed, or both lipid and protein removed (MF‐L‐P) were subjected to digestion assays. The in vitro starch digestibility and eGI of the millet samples and millet starch were compared with rice or wheat flour. Rapidly digestible starch, slowly digestible starch, and resistant starch (RS) of the samples were also calculated. Protease treatment and defatting resulted in significant reduction (P < 0.05) in protein and lipid contents of samples. Significant increases in the in vitro starch digestibility and eGI of samples were observed after removal of protein, lipid, or both. The effect of lipid removal on in vitro starch digestibility of kodo millet was found to be more significant, compared with when proteins were removed. The eGI increased from 49.4 for cooked MF to 62.5 for MF‐L‐P. The eGI of cooked kodo millet starch was significantly lower than that of cooked rice flour. The RS (1.61%) of cooked rice was the least among the samples. The in vitro starch digestibility and eGI of rice were significantly higher than those of MF. Processes applied to kodo millet, such as decortication, that result in the removal of proteins, lipids, or both (especially lipids) would result in an increase in its in vitro starch digestibility and eGI. We therefore advocate for the development of acceptable products from whole millets to maintain its hypoglycemic property.
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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) | 0.001 | 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".