Nutritional Composition of the Green Leaves of Quinoa (Chenopodium quinoa Willd.)
Bibliographic record
Abstract
Quinoa (Chenopodium quinoa Willd.) grain is often eaten worldwide as a healthy food, but consuming nutrient-rich quinoa leaves as a leafy green vegetable is uncommon. This study evaluated the potentiality of leafy green quinoa as a major source of protein, amino acids, and minerals in the human diet. Also, the study compared the nutrient content of quinoa leaves with those of amaranth and spinach leaves. The proximate analysis of quinoa dry leaves showed a higher amount (g/100 g dry weight) of protein (37.05) than amaranth (27.45) and spinach (30.00 g). Furthermore, a lower amount of carbohydrate (34.03) was found in quinoa leaves compared to amaranth (47.90) and spinach (43.78 g). A higher amount of essential amino acids was found in quinoa leaves relative to those of amaranth and spinach. The highest amounts (mg/100 g dry weight) of minerals in quinoa dry leaves were copper (1.12), manganese (26.49), and potassium (8769.00 mg), followed by moderate amounts of calcium (1535.00), phosphorus (405.62), sodium (15.12), and zinc (6.79 mg). Our findings suggest that quinoa leaves can be consumed as a green vegetable with an excellent source of nutrients. Therefore, we endorse the inclusion of quinoa in the leafy green vegetable group.
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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 | 0.001 | 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".