In this work, we show that the projections of the predictors on the normalized regression vectors represent a target rotation with the responses (concentration vectors) as targets. By means of this operation the predictive ability of a latent-variable (LV) regression model and the importance of each predictor for all the responses is obtained. The two features can be portrayed simultaneously and quantitatively in an LV regression BIPLOT display. This graph shows how modelled interferents influence prediction, information as important as the detection of and correction for unmodelled interferents when using a regression model for prediction.
For samples characterized by whole digital profiles rather than a collection of peaks, graphs showing the covariances between the responses and the original or the reproduced predictor space appear to provide the most useful information for interpreting an LV regression model.