Decoding complex multicomponent chromatograms by Fourier Analysis

The present work discusses the many attributes - classified as observable, intrinsic or hidden - which can be conceived for any complex multicomponent chromatogram. Discussion ensues on how to decode such chromatograms, i.e. determining the intrinsic and/or hidden attributes from those which can be observed. There are two main steps. The first is based on Fourier Analysis (FA) and determines the intrinsic attributes: i.e., the number of single components which can be detected; their distribution over the available chromatographic space and peak capacity. The second evaluates the hid den attributes: i.e., the effects of incomplete separation, the number of peaks created by one or more single components as well as their degree of purity. The hidden attributes can be obtained by applying the theory of Statistical Degree of peak Overlapping (SDO) and the paper goes into the extent to which the SDO step depends on the FA results. In addition, the role Exponential distribution plays as a point of reference for the distribution of both single component peak position interdistances and peak heights is discussed. Finally, a simplified graphical FA procedure is presented and the main achievements in this field are reviewed.