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Investigation of nonlinear orthogonal signal correction algorithm and its effects on multivariate calibration

The aim of this paper is to develop a nonlinear orthogonal signal correction (OSC) algorithm using kernel-based technique, termed as kernel OSC (KOSC), and investigate its effects on multivariate calibration. As a nonlinear data pretreatment, the proposed KOSC method can better analyze the nonlinear relationships between descriptor and response variables and remove from process measurement those undesirable variations not correlated with process property from a nonlinear point of view, which well prepares the corrected process trajectory for the subsequent calibration modeling. Two data sets are employed in illustration experiment. It is found that nonlinear OSC plus nonlinear calibration algorithm seems to have the superiority over other methods to improve the interpretation ability of regression model when process data nonlinearly vary with quality.