Hard modeling methods for the curve resolution of data from liquid chromatography with a diode array detector and on-flow liquid chromatography with nuclear magnetic resonance spectroscopy.
Hard modeling methods have been performed on data from high-performance liquid chromatography with a diode array detector (LC-DAD) and on-flow liquid chromatography with 1H nuclear magnetic spectroscopy (LC-NMR). Four methods have been used to optimize parameters to model concentration profiles, three of which belong to classical optimization methods (the simplex method of Nelder-Mead, sequential quadratic programming approach, and Levenberg-Marquardt method), and the fourth is the application of genetic algorithms using real-value encoding. Only classical methods worked well for LC-DAD data, while all of the methods produced good results when LC-NMR data were divided into small spectral windows of peak clusters and parameters were optimized over each window.
Wasim
Mohammad
M
School of Chemistry, University of Bristol, Cantock's Close, BRISTOL BS8 1TS, United Kingdom.
Brereton
Richard G
RG