The use of statistical overlap theory for the deconvolution of complex chromatographic data is extended to applications including two-dimensional (2-D) separations. In the approach employed by Professor Davis and his students at Southern Illinois University, the extent of overlap of co-eluting components can be predicted as well as computation of the required peak capacity to reduce the degree of overlap to manageable levels. A key component to this program is the distribution of the generated mathematic codes via the internet so that practitioners can have direct access to the deconvolution methodologies. Chromatographic separations are a vital component in many chemical analyses. Analytical separations remove some of the ambiguities present in many detection methods, and thus the ability to identify the presence of co-eluting species is of utmost importance. The wide variety of separation media and underlying phenomena make the development of universal computational methods to deconvolve overlapping peaks very difficult. The continued development of the statistical overlap theory and placement of the computational capabilities on the internet by Professor Davis and his students may serve to address the needs of a wide range of chromatography applications.
