Piecewise orthogonal signal correction

A novel signal-processing method that performs orthogonal signal correction (OSC) in a piecewise manner, namely piecewise OSC (POSC), is developed and applied to two near-infrared (NIR) data sets of multivariate calibration. Partial least squares (PLS) regression models were constructed for the POSC-corrected spectra, and the results were compared with those obtained by the Wise and Fearn OSC algorithms. It is shown that performing POSC prior to calibration yields regression models that are more parsimonious (fewer latent variables) and with better predictive power than models obtained by the above methods. The removal of orthogonal components from the response matrix is greatly facilitated simply by considering localized spectral features.