Rapid determination of FeO content in sinter ores using DRIFT spectra and multivariate calibrations

Using diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), several multivariate calibration methods are explored for the quantitative determination of FeO content in sinter ores. The multivariate calibrations include ridge regression with variable selection, principal component regression, ridge principal component regression, and partial least square regression with the linear and the nonlinear mapping using neural networks. Spectral data are preprocessed by signal correction and scaling prior to the modeling. Cross validation is employed to obtain the optimal biasing parameter in ridge-related regression and to obtain the optimal number of principal components (or latent variables) in component-related modeling. We consider the possibility of reducing the number of variables involved in models while maintaining the prediction power to propose a final prediction model. For the quantitative determination of FeO content in sinter ores, component related regressions on auto-scaled orthogonal signal correction are suggested as appropriate calibration methods.