Occurrence of Aflatoxin in Some Food Commodities Commonly Consumed in Nigeria
Bibliographic record
Abstract
Aflatoxicosis is a public health problem in Nigeria like other tropical and sub-tropical regions of the world. Control of aflatoxin contamination requires thorough risk assessment, monitoring, quality control and empirical data. This study assayed total aflatoxin levels, identified and quantified four aflatoxin types in five food commodities commonly consumed in the six geopolitical zones of Nigeria. The food materials: Zea mays, Colocynthis citrullus, Capsicum frutescens, Irvingia gabonensis and Arachis hypogea were obtained from Watt market in Calabar urban. ELISA method was used for total aflatoxin, HPLC for aflatoxin types, AOAC for moisture. All (100%) the samples were contaminated with aflatoxin. Contamination was highest in Irvingia gabonensis (63.40 ± 1.79 µg/kg) and least in Zea mays (3.20 ± 0.12 µg/kg) (p < 0.05). Except for Irvingia gabonensis and Colocynthis citrullus, total aflatoxin was within safe intake level of the Nigerian regulatory authority (National Agency for Food and Drug Adminstration and Control {NAFDAC}). All four aflatoxin types occurred in Irvingia gabonensis, Capsicum frutescens and Colocynthis citrullus; none was detected in Arachis hypogea. AFB1 contamination was highest in Irvingia gabonensis (11.71±0.10 µg/kg) followed by Capsicum frutescens (1.21 ± 0.01 µg/kg); AFB2 ranged from 0.00 ± 0.00-2.43 ± 0.05 µg/kg, AFG1 0.00 ± 0.00-3.73 ± 0.04 µg/kg, and AFG2 0.00 ± 0.00-0.54 ± 0.01 µg/kg (p < 0.05). Only Irvingia gabonensis exceeded the limit of AFB1 specified by NAFDAC for human foods. Moisture content varied widely (3.23 ± 0.03%-10.37 ± 0.19%). The trend in the occurrence of aflatoxins in the food samples was directly proportional (r = 0.91) to their moisture contents. Food commodities sold in Calabar carry potential health hazard. Improved handling through food processing, preservation and storage can minimize aflatoxins in foodstuffs and ensure sustainable quality of food supply.
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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 | 0.000 | 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 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".