Chemical composition of Moringa (Moringa oleifera) root powder solution and effects of Moringa root powder on E. coli growth in contaminated water
Bibliographic record
Abstract
There are many methods available to treat contaminated drinking water; however, economic, cultural, and social factors often impair implementation of these methods, particularly in developing countries. Moringa root powder seems to offer a promising alternative to treating contaminated water. Roots were extracted from randomly selected, seven month old plants, grown in a greenhouse. The roots were washed, bark peeled, oven-dried and ground into powder. Solubility of Moringa root powder was examined by mixing the dried powder in nine different Moringa concentrations (12.5, 27.5, 250, 1250, 2500, 4200, 8300, 12,500, and 16,000 mg/L). Four treatments (0, 250, 450, and 600 mg/L) of Moringa concentrations were used to determine their effectiveness at reducing Escherichia coli in water from a mixed livestock farm pond. Each treatment was added to two (50 and 37 MPN/100 mL) concentrations of E. coli contaminated water. Potassium, sodium, magnesium, phosphorus and calcium were the most abundant macronutrients in Moringa root powder solutions. Low levels of zinc, iron and copper were also detected. At the highest concentration (600 mg/L), and higher initial E. coli concentration (50 MPN/100 mL), Moringa root powder reduced E. coli colonies in contaminated water by 87% (p < .05). Moringa root powder showed strong antimicrobial activity against E. coli and the efficacy of this method should be investigated to determine whether further reduction in bacteria can be achieved, since roots can be harvested sooner than seeds and are available throughout the year.
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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".