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Carmen Paz Suárez Araujo Patricio García Báez Álvaro Sánchez Rodríguez José Juan Santana Rodríguez 《Analytical and bioanalytical chemistry》2009,394(4):1059-1072
In this paper, we approach, using neural computation and ensemble systems, a pattern classification problem in fluorescence
spectrometry, the resolution of difficult multi-fungicide mixtures (overlapping), specifically the benzimidazole fungicides,
benomyl, carbendazim, thiabendazole and fuberidazole. These fungicides are compounds of an important environmental interest.
Because of this, from an analytical point of view, it is interesting to develop sensitive, selective and simple methods for
their determination. Fluorescence spectrometry has proven to be a sensitive and selective technique for determination of many
compounds of environmental interest, but in some cases it is not enough. HUMANN is a hierarchical, unsupervised, modular,
adaptive neural net with high biological plausibility, which has shown to be suitable for identification of these fungicides
and organochlorinated compounds of environmental interest. We propose two modular artificial intelligent systems, with a structure
of pre-processing and processing stage, a multi-input HUMANN-based system, using multi-fluorescence spectra as input to the
system, and a HUMANN-ensemble system. We analyze the optimal configuration of inputs and the ensemble in order to obtain better
results. We study such figures as precision and sensitivity of the method. Our proposal is a smart, flexible and effective
complementary method, which allows reducing the analytical and/or computational complexity of the analysis.
Figure Stages in identification of benzimidazole fungicides
Based on a contribution presented at the XIII International Symposium on Luminescence Spectrometry held in Bologna, Italy
from Sept. 7–11, 2008. 相似文献
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