On orthogonal signal correction

A new algorithm for orthogonal signal correction is presented, compared with existing algorithms, and illustrated on an example from near infrared spectroscopy. Given a matrix X of spectral or other high dimensional data and a vector or matrix Y of concentrations or other reference measurements on the same samples, orthogonal signal correction subtracts from X factors that account for as much as possible of the variance in X and are orthogonal to Y. The aim is to improve the performance of a subsequent partial least squares (PLS) regression of Y on X.