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Keywords:

  • multiblock PLS;
  • non-linear regression;
  • latent variables

Abstract

A novel multiblock PLS algorithm called S-PLS (serial PLS) is presented. S-PLS models the separate predictor blocks serially, making it a supplement to hierarchical PLS. In the S-PLS algorithm the predictor blocks are connected only via the response Y. The block models are calculated using the Y residuals from the previous block model. This allows for an independent interpretation of the separate block models. In each block model the classical PLS algorithm is used. The principles of S-PLS are demonstrated on two chemical applications. In the first example, which is non-linear, S-PLS makes it possible to separate the linear and non-linear parts in the model. The second example illustrates how a model with two predictor blocks can be analysed with S-PLS. Copyright © 1999 John Wiley & Sons, Ltd.