title: Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC.
abstract: Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network-genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer's desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples.
authors.affiliation: Department of Biological Sciences, California State University Los Angeles, Los Angeles, CA 90032, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Jansen
authors.forename: Jacqueline
authors.initials: J
authors.lastname: Kiwata
authors.forename: Jennifer
authors.initials: J
authors.lastname: Arceo
authors.forename: Kym F
authors.initials: KF
authors.lastname: Faull
authors.forename: Grady
authors.initials: G
authors.lastname: Hanrahan
authors.forename: Edith
authors.initials: E
authors.lastname: Porter
mesh_terms: D002788:Cholesterol Esters / Q000302:isolation & purification*; D002851:Chromatography, High Pressure Liquid / Q000379:methods*; D006801:Humans; D007113:Immunity, Innate; D008895:Milk, Human / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer
chemical_list: D002788:Cholesterol Esters