Determination of Multi-components in Marine Sediments by Core Scanner Based on BP Neural Network of Genetic Algorithm

Marine sediments have complex components. Due to the influence of the matrix effect, intensities of elements can only be acquired when using a Core Scanner to carry out X-ray Fluorescence Spectrum analysis, which restricts its application in the fields of paleoecology and mineralization. A method has been introduced for the fast determination of Al₂O₃, K₂O, CaO, TiO₂, MnO, Fe₂O₃, V, Cr, Cu, Zn, Rb, Sr, Y and Pb in marine sediments by Core Scanner, the effects of back-propagation neural network on correcting the nonlinear matrix effects have been investigated and are presented in this paper. Experimental results show that using national certified reference materials of stream sediments, marine sediments and rocks as training samples, a genetic algorithm is used to optimize the initial weight and bias of BP neural network. The matrix effect of 14 elements except Si was corrected by the GA-BP neural network method, which converts the Core Scanner X-ray Fluorescence Spectrum output results from intensities to concentrations. The relative standard deviations of this method are 0.6%-6.8% (n=11). The relative deviations between the predicted values and the reference values of the 15 components of the national standard materials and marine sediment samples range from 0.5% to 17.5%. This indicates that the proposed method is suitable for fast analysis of multi-components in marine sediments, extending the functions of the Core Scanner.