title: Quantitative analysis of cefalexin based on artificial neural networks combined with modified genetic algorithm using short near-infrared spectroscopy.
abstract: In this paper, a novel chemometric method was developed for rapid, accurate, and quantitative analysis of cefalexin in samples. The experiments were carried out by using the short near-infrared spectroscopy coupled with artificial neural networks. In order to enhancing the predictive ability of artificial neural networks model, a modified genetic algorithm was used to select fixed number of wavelength.
authors.affiliation: College of Chemistry, Jilin University, Changchun 130021, China.
authors.forename: Yanfu
authors.initials: Y
authors.lastname: Huan
authors.forename: Guodong
authors.initials: G
authors.lastname: Feng
authors.forename: Bin
authors.initials: B
authors.lastname: Wang
authors.forename: Yulin
authors.initials: Y
authors.lastname: Ren
authors.forename: Qiang
authors.initials: Q
authors.lastname: Fei
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000032:analysis*; D002506:Cephalexin / Q000032:analysis*; D016571:Neural Networks, Computer; D019265:Spectroscopy, Near-Infrared / Q000191:economics / Q000379:methods*
chemical_list: D000900:Anti-Bacterial Agents; D002506:Cephalexin