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Background: Contamination by mycotoxins is a major concern to the maize industry in north-east Italy where maize grain is often spoiled by Fusarium spp. In this work, fumonisins, deoxynivalenol and zearalenone were determined and an artificial neural network (ANN) model suitable for predicting mycotoxin contamination of maize at harvest time was developed., Results: The occurrence of deoxynivalenol and zearalenone was very limited, while fumonisins concentration ranged from 163 and to 3663 µg kg(-1) in 2007, and from 333 to 11473 µg kg(-1) in 2008. Statistical data analysis of factors affecting fumonisins concentration revealed that irrigation, chemical treatment against the European corn borer and harvest date significantly affected the level of contamination (P < 0.05), although the relevance of the factors was different in 2007 and 2008. The neural network approach showed a significant correlation between ascertained values and predictions based on agronomic data., Conclusion: This is the first time that an artificial neural network has been used to predict fumonisin accumulation in maize: the prediction has been shown to have the potential for the development of a new approach for the rapid cataloging of grain lots., (Copyright © 2012 Society of Chemical Industry.)

Background: In maize-growing areas where fumonisin contamination is endemic, there is an urgent need for novel methods to assess the quality of grain lots before their delivery to common drying and storage collection centres. Aerobiological samples of fungal spores released during harvest were analysed to establish a relationship between fumonisin contamination and the abundance of pathogen propagules collected in the combine harvester using a cyclone and membrane filters. Filter-captured propagules were analysed by direct plating, immunoenzymatic assay of specific Fusarium extracellular polysaccharides and real time polymerase chain reaction of the extracted DNA using fum1, a gene involved in the biosynthesis of fumonisin, as a target., Results: The results showed that time of harvest and environmental conditions strongly influenced the efficiency and performance of the collection system. The data obtained were informative in comparing individual samples collected under similar conditions. The immunoenzymatic assay provided the most reliable data, which improved the ability of a neural network to predict the fumonisin content of lots, when added to agronomic, environmental and phytosanitary data., Conclusion: This is the first attempt to evaluate the Fusarium propagules dispersed during harvesting as a predictive means to assess maize quality. A method based on cyclone/filter capture and immunological detection has been shown to be feasible and to have the potential for the development of a continuous monitoring system, but the prediction capabilities in the present implementation were limited.