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Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies
Authors:Carmen Paz Suárez-Araujo  Patricio García Báez  Álvaro Sánchez Rodríguez  José Juan Santana-Rodrríguez
Institution:1.Instituto Universitario de Ciencias y Tecnologías Cibernéticas,Universidad de Las Palmas de Gran Canaria,Las Palmas de Gran Canaria,Spain;2.Departamento de Ingeniería Informática y de Sistemas,Universidad de La Laguna,La Laguna,Spain;3.Instituto de Estudios Ambientales y Recursos Naturales,Universidad de Las Palmas de Gran Canaria,Las Palmas de Gran Canaria,Spain
Abstract:Benzimidazole fungicides (BFs) are a type of pesticide of high environmental interest characterized by a heavy fluorescence spectral overlap which complicates its detection in mixtures. In this paper, we present a computational study based on supervised neural networks for a multi-label classification problem. Specifically, backpropagation networks (BPNs) with data fusion and ensemble schemes are used for the simultaneous resolution of difficult multi-fungicide mixtures. We designed, optimized and compared simple BPNs, BPNs with data fusion and BPNs ensembles. The information environment used is made up of synchronous and conventional BF fluorescence spectra. The mixture spectra are not used in the training nor the validation stage. This study allows us to determine the convenience of fusioning the labels of carbendazim and benomyl for the identification of BFs in complex multi-fungicide mixtures.
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