Recent advances in fuzzy peak tracking in high-performance liquid chromatography

Abstract In general, automated optimization procedures for chromatographic separations necessitate a recognition of the eluted signals. An approach based on comparison of peak areas and of the elution order of the peaks has been designed to match the requirement of widely varying chromatographic conditions. This method relies on fuzzy theory and can therefore be appliled to uncertain data as they stem from the impreceision of peak areas, the change in the elution order and the uncertainty of overlapped peak areas. The handling of peak overlap has been greatly improved and is successfully demonstrated for the recognition of chromatograms with several overlapped peaks and changing elution patterns.