The use of the Durbin-Watson criterion for noise and background reduction of complex liquid chromatography/mass spectrometry data and a new algorithm to determine sample differences

Recently, Massartʹs research group showed the potential of the Durbin-Watson criterion as a tool to determine the pseudo-rank of mixture data sets using stepwise interactive self-modeling mixture analysis approaches. The Durbin-Watson criterion is a simple and intuitive equation which works surprisingly well for other data analysis problems. As an example, the use of the Durbin-Watson criterion will be introduced in this paper as a tool to reduce noise and baseline problems in liquid chromatography/mass spectrometry (LC/MS) by variable reduction. In addition, another algorithm to extract differences between highly related samples will be presented. Both new approaches have some advantages above previously published methods.