Aflatoxin variations in maize flour and grains collected from various regions of Kenya
Bibliographic record
Abstract
In Kenya, maize remains an important staple food in every household. Unfortunately, the fungus Aspergillus flavus can infect the maize and produce aflatoxins. While government efforts to remove contaminated maize from circulation are well intentioned, there remain concerns that consumers are still being inadvertently exposed to aflatoxin. The aim of this study was to sample maize in different parts of Kenya and determine if consumers were inadvertently being chronically exposed to aflatoxins. Seventy-five maize samples and 27 samples of maize flour from three regions of Kenya (Nairobi, Eastern and Western) were analysed using an ELISA assay followed by microtiter plate reader (Neogen model) where the optical density of each microwell was read using a 450nm filter. There was a significant difference in aflatoxin levels in maize grains between the three regions and five stores (P<0.05). Samples from Eastern Kenya had the highest contamination at 22.544.94 ppb, while those from Nairobi had the lowest (7.921.57 ppb). There was no significant difference in the total aflatoxin in maize flours from Nairobi, Western and Eastern regions (P>0.05) at 95% confidence interval. Aflatoxin in maize flours were slightly above international upper limit of 5ppb but all the results were lower than the Kenya standard whose upper limit is 10ppb, indicating good manufacturing practices (GMP) by the millers. Samples of maize flours from Eastern Kenya had the highest aflatoxins concentrations at 6.98 0.53 ppb. In summary, the study found aflatoxin contamination in maize grains especially in Eastern Kenya. The study concluded that measures put in place by government agencies for millers appear to be working. However, samples of maize grains showed variation among the regions and between stores, perhaps due to storage practices, with some levels far exceeding health limits. Due to higher levels of aflatoxin contamination in maize grains in relation to maize flours, the government and relevant stakeholders need to establish further measures to protect 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".