Interelement coefficients, prediction

FIGURE 6.30 Calibration graph of a trace element before and after correction with interelement coefficients predicted by a CPG nenral network. Using raw nncorrected countrates from the instrument (gray line) leads to a poor correlation inappropriate for analytical determination. After correction with the predicted matrix-specific interelement coefficients, the calibration leads to a reasonable regression line. 

A CPG neural network can help to find the appropriate interelement coefficients by training the network with pairs of descriptors, one of which contains the raw count rates for the interfering elements whereas the other contains experimentally determined interelement coefficients (Figure 6.29). Training the network with values of a well-defined type of chemical matrix, the network is able to predict the interelement coefficients that can finally be used to correct the calibration graph used for determining the element concentrations (Figure 6.30). 

FIGURE 6.29 Schematic view of a CPG neural network trained with vector pairs. The input vector consists of ten countrates for major elements in rock samples, whereas the output vector contains the interelement coefficients p for all major elements. After training, the network is able to predict the interelement coefficients for the given sample matrix. 

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See also in sourсe #XX -- [ , ]

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