Physicochemical characterization of a composite flour: Blending purple sweet potato and rice flours
Bibliographic record
Abstract
In this study, the physicochemical characterization of different ratios of purple sweet potato flour (PSPF) and rice flour was investigated to improve the nutritional value and enrich the variety of rice-based staple food. The results showed that adding PSPF increased total dietary fiber and anthocyanin content whereas decreased amylose content of the composite flours. Additionally, the composite flours exhibited lower thermodynamic parameters and displayed darker, redder, and bluer colors. There were no noticeable changes in the functional group structure of the composite flours. The addition of PSPF decreased the crystallinity and water-holding capacity of the composite flours, whereas increased the average particle size and iodine blue value. PSPF increased the pasting temperature of the flours whereas decreased the breakdown and setback values. Overall, the addition of PSPF significantly affects the nutrition, color and physicochemical properties of the composite flours.
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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".