Phenolic Composition and Antioxidant Properties of Cooked Rice Dyed with Sorghum-Leaf Bio-Colorants
Bibliographic record
Abstract
White rice is an important staple food globally. It is a rich source of energy but is low in dietary phenolic antioxidants. This current research aimed at providing scientific evidence for an alternative rice dish that has increased phenolic-antioxidant health-promoting potential by combining white rice with red cowpea beans and cooking with dye sorghum leaves hydrothermal extract, as a source of natural colorant. Boiled white rice and the rice–cowpea–sorghum extract dish were freeze-dried, and the free and bound phenolic compounds of raw and cooked samples were extracted. Phenolic composition, total phenolic content (TPC), and antioxidant activities (measured by 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity, Trolox equivalent antioxidant capacity, and oxygen radical absorbance capacity methods) of the raw and cooked samples were determined. Combining white rice with cowpea seeds and sorghum leaves extract significantly (p < 0.0001) increased the TPC and antioxidant activities of the rice due to the higher TPC and antioxidant activities of cowpea and sorghum leaves. Although boiling caused substantial losses of flavonoids and anthocyanins in the rice–cowpea–sorghum extract composite meal, the resulting dish had higher TPC and antioxidant activities than boiled white rice. Compositing white rice with phenolic-rich pulses can be an innovative approach to providing alternative healthy rice dishes to consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".