Abstract
This paper suggests a novel method named DOSC-DF-GRNN, which is based on generalized regression neural network (GRNN) combined with direct orthogonal signal correction (DOSC) and data fusion (DF) to enhance the ability to extract characteristic information and improve the quality of the regression for the simultaneous simultaneous diffrential pulse voltammetric determination of Ni(II), Zn(II) and Co(II). In this case, the relative standard errors of prediction (RSEP) for total elements with DOSC-DF-GRNN, DOSC-GRNN, DF-GRNN, GRNN and PLS were 9.70, 10.8, 11.5, 12.2 and 12.3 %, respectively. Experimental results showed the DOSC-DF-GRNN method was successful for diffrential pulse voltammetric determination even when there were severe overlaps of voltammograms existed and was the best among the five methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Marini, F., Magri, A.L., Bucci, R., Magri, A.D.: Use of different artificial neural networks to resolve binary blends of monocultivar Italian olive oils. Anal. Chim. Acta 599(2), 232–240 (2007)
Verikas, A., Bacauskien, M.: Using artificial neural networks for process and system modeling. Chemomotr. Intell. Lab. Syst. 67, 187–191 (2003)
Choi, S.W., Lee, D., Park, J.H., Lee, I.B.: Nonlinear regression using RBFN with linear submodels. Chemometr, Intell. Lab. Syst. 65, 191–208 (2003)
Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)
Specht, D.F.: A general regression neural network. IEEE Transactions on Neural Network 2(6), 568–576 (1991)
Wold, S., Antti, H., Lindgren, F., Ohman, J.: Orthogonal signal correction of near-infrared spectra. Chemometr. Intell. Lab. Syst. 44, 175–185 (1998)
Westerhuis, J.A., de Jong, S., Smilde, A.G.: Direct orthogonal signal correction. Chemometr. Intell. Lab. Syst. 56, 13–25 (2001)
Daubechies, I.: Orthogonal bases of compactly supported wavelets. Commun. Pure Appl. Math. 41, 909–996 (1988)
Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets. IEEE Trans. Inform. Theory 38(2), 617–643 (1992)
Ren, S.X., Gao, L.: Simultaneous quantitative analysis of overlapping spectrophotometric signals using wavelet multiresolution analysis and partial least squares. Talanta 50(6), 1163–1173 (2000)
Gao, L., Ren, S.X.: Combining direct orthogonal signal correction and wavelet packet transform with partial least squares to analyze overlapping voltammograms of nitroaniline isomers. Electrochimica Acta 54, 3161–3168 (2009)
Ruhm, H.: Sensor fusion and data fusion-mapping and reconstruction. Measurement 40, 145–157 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, S., Gao, L. (2011). Resolve of Multicomponent Mixtures Using Voltammetry and a Hybrid Artificial Neural Network Method. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-23896-3_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
eBook Packages: Computer ScienceComputer Science (R0)