Model Based Preprocessing and Background Elimination : OSC, OPLS, and O2PLS

Model-based preprocessing using orthogonal signal correction (OSC) is a procedure with a well-defined objective: to remove variation that is linearly independent (orthogonal) of a given response matrix. The properties and applicability of OSC filtering are discussed. Recent extensions of OSC, including orthogonal projection to latent structures (OPLS) (comparable to projection to latent structures (PLS) with an integral OSC filter), O2PLS (bidirectional OPLS), and K-OPLS (nonlinear OPLS), are outlined and discussed.