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.KeywordsVoltammetryArtificial neural networkDirect orthogonal signal correctionData fusionGeneralized regression neural network
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