Anti-Nutrient, Phytochemical and Antiradical Evaluation of 10 Amaranth (Amaranthus spp.) Varieties Before and After Flowering
Bibliographic record
Abstract
Amaranth (Amaranthus spp.) vegetable is widely consumed in Kenya and contributes to the alleviation of food insecurity. It is reported to have bioactive components such as antioxidants that help in protecting the body from long-term degenerative diseases. However, amaranth vegetable has also been shown to contain some anti-nutrients such as tannins, phytic acid, oxalates and nitrates which may bind nutrients and reduce their bioavailability in the body. There are many amaranth varieties and there is very little information about differences in the anti-nutrient and phytochemical levels among the varieties. The objective of this study was to determine the phytochemical and anti-nutrient content of ten amaranth varieties at two growth stages, before and after flowering. The study was in Jomo Kenyatta University of Agriculture and Technology. Ten amaranth varieties, eight of which are new varieties, were planted. Harvesting was done in two stages, at vegetative stage and post flowering stage. The leaves were analyzed for nitrates, vitamin C, total carotenoids, flavonoids and total antioxidant activity. Results showed decrease in nitrates which reduced by about 40% as the plants matured. The leaves also increased antioxidant activity as the plants matured with the concentration equivalent (IC50) with DPPH of 2 mg/mL at vegetative stage and about 1mg/mL after flowering. In conclusion, as the plants grow older, there was an increased accumulation of anti-nutrient and other plant chemicals. Antioxidant activity is however increased.
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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".