Abstract
Orthogonal Projection to Latent Structures (OPLS) is a preprocessing method that was presented as an improvement of the PLS algorithm it was issued from. Nevertheless, according to the bibliography its added value is questionnable both for prediction and interpretation. To contribute to a better understanding, we investigated the relationship between OPLS and the Net Analyte Signal (NAS). For four numerical applications, the matrix obtained after the OPLS deflation tended towards a matrix of rank 1 when the number of removed dimensions increased. Therefore, the row-vectors of this matrix are collinear to the NAS, and so the usual one-latent-variable PLS1 regression following the OPLS preprocessing can be replaced by almost any regression method. Moreover, the interpretation relies on a vector of rank one issued from the deflated matrix, which does not bring more than the regular PLS regression vector.