Molecular and thermal characterization of starches isolated from African rice (O<i>ryza glaberrima)</i>
Bibliographic record
Abstract
Starch is the principal component of rice that affects its cooking and nutritional quality. This study investigated molecular and thermal properties of starches isolated from seven Africa rice accessions (ARAs) in comparison with two commonly produced Asian rice varieties (ARVs) and a developed cross (sativa × glaberrima) variety (NERICA 4). All starch granules were polyhedral and tightly packed with size distribution ranging from 2–22 µm and displayed type‐A X‐ray diffraction pattern. ARAs starch granules had higher ratio of absorbance to scattering when exposed to iodine vapor exhibiting greater flexibility and availability of glucan chains to form complexes with iodine as compared to ARVs. The enthalpies of starch gelatinization (15.1–15.8 J/g) and retrograded gel melting (9.2–10.8 J/g) were higher in ARAs than in NERICA 4 (14.5 and 9.2 J/g, respectively) and ARVs, (13.3–14.3 and 6.4–7.3 J/g, respectively) possibly due to their higher amylose content and longer chains. Significant (p < 0.05) differences in peak, trough, final, breakdown, and setback viscosities were also observed among the starches with Koshihikari Asian rice having the highest peak viscosity (310 RVU). These differences in molecular structure and thermal properties between the ARAs and ARVs are likely to influence the cooking and eating quality of the ARAs.
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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".