Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies


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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  1. Almhdi KM, Valigi P, Gulbinas V, Westphal R, Reuter R (2007) Classification with artificial neural networks and support vector machines: application to oil fluorescence spectra. EARSeL eProceedings 6(2):115–129

    Google Scholar 

  2. Bordagaray A, Amigo RJ (2015) Modelling highly co-eluted peaks of analytes with high spectral similarity. Trends Anal Chem 68:107–118. doi:10.1016/j.trac.2015.02.010

    CAS  Article  Google Scholar 

  3. Bro R (1997) PARAFAC. Tutorial and applications, Chemom. Intell. Lab. Syst, chap 38.2, 149–171

  4. Clarke C (2008) Development of an automated identification system for nanocrystal encoded microspheres in flow cytometry. PhD thesis, Cranfield University

  5. D’Archivio AA, Maggi MA, Marinelli C, Ruggieri F, Stecca F (2015) Optimisation of temperature-programmed gas chromatographic separation of organochloride pesticides by response surface methodology. J Chromatogr A 1423:15708. doi:10.1016/j.chroma.2015.10.082.

    Google Scholar 

  6. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. doi:10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  7. Fernández-Sánchez J, Carretero AS, Benítez-Sánchez J, Cruces-Blanco C, Fernández-Gutiérrez A (2004) Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons. Anal Chim Acta 510(2):183–187. doi:10.1016/j.aca.2004.01.012.

    Article  Google Scholar 

  8. Ferrer R, Guiteras J, Beltrán J (1999) Artificial neural networks (ANNs) in the analysis of polycyclic aromatic hydrocarbons in water samples by synchronous fluorescence. Anal Chim Acta 384(3):261–269. doi:10.1016/S0003-2670(98)00778-8,

    CAS  Article  Google Scholar 

  9. Gallinari P (1995) Training of modular neural net systems. In: Arbib M A (ed) Handbook of brain theory and neural networks, MIT press, pp 582–585

  10. García Báez P (2005) HUMANN: una nueva red neuronal artificial adaptativa, no supervisada, modular y jerárquica aplicaciones en neurociencia y medioambiente. PhD thesis, ULPGC

  11. García Báez P, Suárez Araujo C, Fernández López P (2003) A parametric study of HUMANN in relation to the noise: application to the identification of compounds of environmental interest. Syst Anal Model Simul 43(9):1213–28

    Google Scholar 

  12. García Báez P, Suárez Araujo CP, Sánchez Rodríguez A, Santana Rodríguez JJ (2010) Towards an efficient computational method for fluorescence identification of fungicides using data fusion and neural ensemble techniques. Luminescence 25(3):285– 287

    Google Scholar 

  13. García Báez P, lvarez Romero Y, Suárez Araujo CP (2012) A computational study on supervised and unsupervised neural architectures with data fusion for fluorescence detection of fungicides. Luminescence 27:534–572

    Article  Google Scholar 

  14. Hansen L, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12 (10):993–1001. doi:10.1109/34.58871

    Article  Google Scholar 

  15. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan, New York

    Google Scholar 

  16. He L, Kear-Padilla L, Lieberman S, Andrews J (2003) Rapid in situ determination of total oil concentration in water using ultraviolet fluorescence and light scattering coupled with artificial neural networks. Anal Chim Acta 478(2):245–258. doi:10.1016/S0003-2670(02)01471-X,

    CAS  Article  Google Scholar 

  17. Henry R (2003) Multivariate receptor modeling by N-dimensional edge detection. Chemom. Intell. Lab. Syst, chap 65.2, 179–189

  18. Johansson U, Löfström T (2012) Producing implicit diversity in ann ensembles. In: Neural networks (IJCNN), The 2012 International Joint Conference on, pp 1–8

  19. Jolliffe IT (2002) Principal component analysis 2nd edn. Springer.

  20. Jones E, Oliphant T, Peterson P, et al. (2001) SciPy: open source scientific tools for Python.

  21. Liu X, Yao Y, Higuchi T (2003) Designing neural network ensembles by minimising mutual information. In: Mohammadian M, Sarker R. A., Yao X (eds) Computational intelligence in control, Hershey : Idea Group Pub, USA & London (UK), pp 1–21

  22. Loewy R (2000) Plaguicidas en aguas subterráneas del alto valle de ríbo negro neuquén. tesis de maestría en ciencias químicas Master’s thesis, Universidad Nacional de Comahue, Argentina

  23. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9(3):10–20. doi:10.1109/MCSE.2007.58

    CAS  Article  Google Scholar 

  24. Paatero P, Tapper U (1994) Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values, Environmetrics, chap 5.2, 111–126

  25. Piccirilli GN, Escandar GM (2006) Partial least-squares with residual bilinearization for the spectrofluorimetric determination of pesticides. A solution of the problems of inner-filter effects and matrix interferents. Analyst 131:1012–1020. doi:10.1039/B603823A

    CAS  Article  Google Scholar 

  26. Rumelhart DE, Hinton GE, McClelland JL (1987) A general framework for parallel distributed processing. In: Rumelhart D E, McClelland J L et al. (eds) Parallel distributed processing, vol 1. Foundations, MIT Press, Cambridge, pp 45–76

  27. Sabik H, Jeannot R (1998) Determination of organonitrogen pesticides in large volumes of surface water by liquid-liquid and solid-phase extraction using gas chromatography with nitrogen-phosphorus detection and liquid chromatography with atmospheric pressure chemical ionization mass spectrometry. J Chromatogr A 818 (2):197–207. doi:10.1016/S0021-9673(98)00555-X,

    CAS  Article  Google Scholar 

  28. Sanger TD (1989) Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Netw 2:459–473. doi:

  29. Santana-Rodríguez JJ, Torres-Padrón ME, Aufartová J, Sosa-Ferrera Z (2010) Fungicides. Benzimidazole fungicides in environmental samples: extraction and determination procedures. Ed:Odile Carisse, InTech, Department of Chemistry. Faculty of Marine Sciences. University of Las Palmas de Gran. Department of Analytical Chemistry, Faculty of Pharmacy, Charles University, chap 15, pp 305–324. 978-953-307-266-1. doi:10.5772/10481

  30. Suarez Araujo CP, García Báez P, Hernández Trujillo Y (2010) Fungicides. Neural computation methods in the determination of fungicides. In: Carisse O (ed) Intech, chap 23

  31. Suárez Araujo CP (1999) Neural computation approach in luminescence spectrometry. Biomed Chromatogr 13(2):187–188 . doi:10.1002/(SICI)1099-0801(199904)13:2%3C187::AID-BMC877%3E3.0.CO;2-E

    Article  Google Scholar 

  32. Suárez Araujo CP, García Báez P, Sánchez Rodríguez A, Santana Rodríguez JJ (2006) Design of a HUMANN-based method for the determination of benzimidazole fungicides with fluorescence detection. Luminescence 21(6):342–344

    Google Scholar 

  33. Suárez Araujo CP, García Báez P, Sánchez Rodríguez A, Santana Rodríguez JJ (2009) HUMANN-based system to identify benzimidazole fungicides using multi-synchronous fluorescence spectra: an ensemble approach. Anal Bioanal Chem 394(4):1059–1072

    Article  Google Scholar 

  34. Todeschini R, Galvagni D, Vílchez J, del Olmo M, Navas N (1999) Kohonen artificial neural networks as a tool for wavelength selection in multicomponent spectrofluorimetric {PLS} modelling: application to phenol, o-cresol, m-cresol and p-cresol mixtures. TrAC, Trends Anal Chem 18(2):93–98. doi:10.1016/S0165-9936(98)00097-1,

    CAS  Article  Google Scholar 

  35. Vasilescu J, Marmureanu L, Carstea E (2011) Analysis of seawater pollution using neural networks and channels relationship algorithms. Rom J Phys 56(3-4):530–539

    CAS  Google Scholar 

  36. Vassilakis Y, Tipi D, Scoullos M (1998) Determination of a variety of chemical classes of pesticides in surface and ground waters by off-line solid-phase extraction, gas chromatography with electron-capture and nitrogen-phosphorus detection, and high-performance liquid chromatoagraphy with post-column derivatization and fluorescence detection, J. Chromatogr. A, chap. 823, 49– 58

  37. Yehia AM, Mohamed HM (2016) Chemometrics resolution and quantification power evaluation: application on pharmaceutical quaternary mixture of paracetamol, guaifenesin, phenylephrine and p-aminophenol. Spectrochim Acta A Mol Biomol Spectrosc 152:491–500. doi:10.1016/j.saa.2015.07.101

    CAS  Article  Google Scholar 

  38. Zhu S, Wu H, Xia A, Ha Q, Zhang Y (2007) Determination of carbendazim in bananas by excitation-emission matrix fluorescence with three second-order calibration methods, Analytical Science, chap 23.10, 1173–1177

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Correspondence to Carmen Paz Suárez-Araujo.

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Suárez-Araujo, C.P., García Báez, P., Sánchez Rodríguez, Á. et al. Supervised neural computing solutions for fluorescence identification of benzimidazole fungicides. Data and decision fusion strategies. Environ Sci Pollut Res 23, 24547–24559 (2016).

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  • Artificial neural networks
  • Fluorescence spectrometry
  • Fungicides
  • Ensembles
  • Mixture resolution
  • Environment
  • Data fusion