Chemometrics in comprehensive multidimensional separations

Chemometric methods have critical importance for the discovery of the information/knowledge buried or concealed in high-dimensional datasets acquired from comprehensive multidimensional separations (CMDS), and for interpretation of experiments or chemical processes. In this work, employment of new developments in chemometrics making full use of the data to maximize the potential of CMDS to resolve mathematically a variety of practical problems is reviewed whilst providing the authors' point of view. During the past several years, chemometrics has been successfully applied to many areas of concern to CMDS investigation, including experimental parameter optimization, data quality improvement, identification and quantification of target chemical components, pattern recognition technique for clustering and classification, multivariate model establishment to correlate chromatographic properties and molecular descriptors. On the basis of the high-dimensionality characteristics of CMDS, some special aspects such as evaluation of orthogonality and image processing have also been included in this review. It is expected that an overview of the diverse ways in which chemometrics can aid CMDS investigations will prove valuable to interested users in this area through a comprehensive survey of previous research contributions. Chemometrics lends itself well to the powerful separation capability of CMDS