Chemical and Mineral Composition of Amaranth (Amaranthus L.) Species Collected From Central Malawi
Bibliographic record
Abstract
Chemical analysis and mineral composition of twenty accessions of grain and leaf Amaranth (Amaranthus L.) collected from different agro-ecological zones of Central Malawi were conducted according to the standards of Association of Official Analytical Chemistry (AOAC). Analysis of variance (ANOVA) and means were separated using least significance difference (P ≤ 0.05) in Gen Stat version 15. The analyses for grain Amaranth showed that moisture content ranges from 10.69 to 12.22% while ash content varied from 4.4 to 8.7%. Elemental analyses in mg/100 grams on dry weight basis indicated that the grain had calcium (78.3 to 1004.6), iron (3.61 to 22.51), magnesium (44.31 to 97.38), potassium (267.8 to 473.6) and zinc (0.53 to 1.20). The mean differences for leaf chemical analyses were highly significant (p < 0.001) with crude protein ranging from 13.37 to 23.27%; ash (14.08 to 19.95%) and Vitamin C (30.3 to 117.79 mg/100 g) while the mean mineral leaf analyses in mg/100 grams ranged from 14.84 to 31.17 for iron, 1.03 to 3.46 for zinc, 1512 to 2381 for calcium, 1320 to 1677 for potassium and 383.4 to 513.9 for magnesium. Generally the accessions from mid altitude area of Lilongwe showed highest values for both grain and leaf mineral analyses while accessions from the high altitude showed lower values. The results of this study provide evidence that local Amaranthus genotypes have appreciable amount of nutrients, minerals and vitamins important to meet dietary requirements of rural and urban communities in Malawi.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".