Aflatoxin B1 contamination in maize related to the aridity index in North Italy

Paola Battilani, Carlo Barbano, Gianfranco Piva

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review


The aim of this study was to develop a prototype simple predictive system for aflatoxin B1 contamination in maize based on meteorological data. A database was developed with meteorological data and aflatoxin B1 contamination level of maize samples collected over a five-year period. All data were georeferenced. An aridity index was computed to summarise meteorological conditions and was used to estimate the probability of aflatoxin B1 contamination running a logistic regression. Relevant differences were found between years both for meteorology and aflatoxin B1 contamination. North Italy is not arid and conditions for Aspergillus flavus development and aflatoxin B1 contamination of maize do not commonly occur. Nevertheless, arid areas were found in some years, and favourable conditions for aflatoxin B1 production were confirmed by maize kernels surveys. The aridity index is a good indicator to summarise meteorological conditions being significantly correlated to maize kernels contamination at harvest. The logistic regression gave acceptable warning on aflatoxin B1 contamination in maize with 64% correct predictions and 23% overestimates. Underestimates were 13%, but only half of these were contaminated with aflatoxin B1 above 5 μg/kg, the European legislative limit for maize to be subjected to sorting or other physical treatment before human consumption or to be used as an ingredient in foodstuffs as well as for complete feedingstuffs for dairy animals. First indications with this simple predictive system are available before mid-July with conclusive information in early September, which is a good time to plan maize management pre- and post-harvest.
Lingua originaleEnglish
pagine (da-a)449-456
Numero di pagine8
RivistaWorld Mycotoxin Journal
Stato di pubblicazionePubblicato - 2008


  • aflatoxin B1
  • geographic pattern
  • logistic regression
  • maize management
  • predictive system


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