Optimisation Of The Separation Of Amino Acids By Capillary Electrophoresis Using Artificial Neural Networks

John Wiley & Sons, Inc
Publication Type:
Chemometric Methods in Capillary Electrophoresis, 2010, 1, pp. 169 - 180
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010002344OK.pdf8.15 MB
Adobe PDF
Many factors can affect the separation performance of a capillary electrophoresis (CE) electrolyte, such as the buffer, surfactant and organic modifier concentrations, pH, capillary temperature, and applied voltage (1). The efficient manipulation of these factors is critical to optimize the resolution of a given analysis in the shortest time frame. During the method development process, an analyst will usually attempt a separation based on a previously reported method that is similar or the same as the requirements of the analysis at hand. If the separation is inadequate, a univariate approach (2) is often employed to attempt to improve the separation. This involves altering one parameter at a time in a systematic way, and viewing the results by plotting the effect of the parameter on the migration time of the analytes. In this way, suitable electrolyte compositions may be found that separates all of the analytes. If suitable conditions are not found, a second electrolyte parameter is chosen and altered in a similar manner. This univariate procedure is then repeated until a suitable condition is found. This method of optimization is time-consuming, and it is unknown if the optimum is truly the global optimum.
Please use this identifier to cite or link to this item: