The quantitative analysis of X-ray fluorescence （XRF） spectra is studied using the partial least-squares （PLS） method. The characteristic variables of spectra matrix of PLS are optimized by genetic algorithm. The ...The quantitative analysis of X-ray fluorescence （XRF） spectra is studied using the partial least-squares （PLS） method. The characteristic variables of spectra matrix of PLS are optimized by genetic algorithm. The subset of multi-component characteristic spectra matrix is established which is corresponding to their concentration. The individual fitness is calculated which combines the crossover validation parameters （prediction error square summation） and correlation coefficients （R^2）. The experimental result indicates that the predicated values improve using the PLS model of characteristic spectra optimization. Compared to the nonoptimized XRF spectra, the linear dependence of processed spectra averagely decreases by about 7%, root mean square error of calibration averagely increases by about 79.32, and root mean square error of cross-validation avera^elv increases by about 14.2.展开更多
文摘The quantitative analysis of X-ray fluorescence （XRF） spectra is studied using the partial least-squares （PLS） method. The characteristic variables of spectra matrix of PLS are optimized by genetic algorithm. The subset of multi-component characteristic spectra matrix is established which is corresponding to their concentration. The individual fitness is calculated which combines the crossover validation parameters （prediction error square summation） and correlation coefficients （R^2）. The experimental result indicates that the predicated values improve using the PLS model of characteristic spectra optimization. Compared to the nonoptimized XRF spectra, the linear dependence of processed spectra averagely decreases by about 7%, root mean square error of calibration averagely increases by about 79.32, and root mean square error of cross-validation avera^elv increases by about 14.2.
