Perennial and seasonal contamination of maize by aflatoxins in eastern Kenya
Bibliographic record
Abstract
Aflatoxin contamination of grain is a major constraint to sustained quality cereal production. The causative fungi, Aspergillus species infect crops in the field and continue to do so post-harvest where they produce toxins in store. The current study aimed at establishing seasonal variation in levels and types of aflatoxins in maize from the Eastern region of Kenya- the hot-spot for aflatoxicosis. Maize kernels were collected from farmers’ households in May and December 2013 -2 months after long rain and short rain season respectively. The total aflatoxins were quantified using Enzyme-Linked Immunosorbent Assay (ELISA), while the toxin composition was determined using Thin-Layer Chromatography (TLC) and confirmed using High-Performance Liquid Chromatography (HPLC). Generally, grain harvested after the long rains (May) had significantly ( p = 0.019) lower aflatoxin levels and variation (5.68 ± 6.31 ppb, 100% Aflatoxin B1) than that of short rains (10.77 ± 10.14 ppb, 72% AFB 1 ). Additionally, from the long and short rain seasons, the samples exceeding regulatory allowed limit (10 ppb) were 16 and 44% respectively. In Eastern Kenya, consumption of maize harvested in the long rain season presents a recurrent risk of exposure to low levels of AFB 1 ; while consumption of maize harvested after the short rain season presents a risk of seasonal exposure to high levels and mixed type of toxins However, this long term risk of exposure to aflatoxins is poorly documented hence these findings necessitate mitigation measures because AFB 1 – is a potent class 1 mutagenic toxin likely to cause liver cancer.
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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".