The Influence of Weather on the Occurrence of Aflatoxin B1 in Harvested Maize from Kenya and Tanzania
| Autor: | J. J. W. Harvey, Benigni A. Temba, Darren J. Kriticos, Anne Gichangi, Philip G. Pardey, James Karanja, Deogratias Lwezaura, Said M. S. Massomo, Noboru Ota, Mary T. Fletcher, Ross Darnell, James M. Wainaina |
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| Rok vydání: | 2021 |
| Předmět: |
Aflatoxin
Veterinary medicine Health (social science) Harvest season Growing season Plant Science Biology maize lcsh:Chemical technology Logistic regression Tanzania 01 natural sciences Health Professions (miscellaneous) Microbiology Article modelling 03 medical and health sciences Aflatoxin contamination lcsh:TP1-1185 climate risk 030304 developmental biology 0303 health sciences Incidence (epidemiology) 010401 analytical chemistry aflatoxin biology.organism_classification Kenya 0104 chemical sciences Warning signs Food Science |
| Zdroj: | Foods, Vol 10, Iss 216, p 216 (2021) Foods Volume 10 Issue 2 |
| ISSN: | 2304-8158 |
| DOI: | 10.3390/foods10020216 |
| Popis: | A study was conducted using maize samples collected from different agroecological zones of Kenya (n = 471) and Tanzania (n = 100) during the 2013 maize harvest season to estimate a relationship between aflatoxin B1 concentration and occurrence with weather conditions during the growing season. The toxins were analysed by the ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method. Aflatoxin B1 incidence ranged between 0&ndash 100% of samples in different regions with an average value of 29.4% and aflatoxin concentrations of up to 6075 µ g/kg recorded in one sample. Several regression techniques were explored. Random forests achieved the highest overall accuracy of 80%, while the accuracy of a logistic regression model was 65%. Low rainfall occurring during the early stage of the maize plant maturing combined with high temperatures leading up to full maturity provide warning signs of aflatoxin contamination. Risk maps for the two countries for the 2013 season were generated using both random forests and logistic regression models. |
| Databáze: | OpenAIRE |
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