Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41395
Title: Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies
Authors: Suárez-Araujo, Carmen Paz 
García Báez, Patricio 
Sánchez Rodríguez, Álvaro
Santana-Rodrríguez, José Juan 
UNESCO Clasification: 251002 Oceanografía química
2301 química analítica
3308 Ingeniería y tecnología del medio ambiente
Keywords: Artificial neural networks
Data fusion
Ensembles
Environment
Fluorescence spectrometry, et al
Issue Date: 2016
Journal: Environmental Science and Pollution Research 
Conference: 1st International Caparica Conference on Pollutant Toxic Ions and Molecules (PTIM)
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.
URI: http://hdl.handle.net/10553/41395
ISSN: 0944-1344
DOI: 10.1007/s11356-016-7129-8
Source: Environmental Science and Pollution Research[ISSN 0944-1344],v. 23, p. 24547-24559
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