Assessment of chromatographic peak purity by means of artificial neural networks.
An improved chemometric approach is proposed for assessing chromatographic peak purity by means of artificial neural networks. A non-linear transformation function with a back-propagation algorithm was used to describe and predict the chromatographic data. The Mann-Whitney U-test was used for the concluding the purity of the chromatographic peak. Simulation data and practical analytical data for both pure and mixture samples were analysed with satisfactory results. A prior knowledge of the impurity and the related compound is unnecessary when a slight difference between their chromatogram and spectrum exists. The performance on simulated data sets by this approach was compared with the results from principal component analysis.
Hu
Y
Y
Department of Analytical Chemistry, China Pharmaceutical University, Nanjing, China.
Zhou
G
G
Kang
J
J
Du
Y
Y
Huang
F
F
Ge
J
J
D000465:Algorithms; D002109:Caffeic Acids / Q000302:isolation & purification; D002845:Chromatography / Q000379:methods*; D006863:Hydrogen-Ion Concentration; D062385:Hydroxybenzoates / Q000302:isolation & purification; D016571:Neural Networks, Computer; D010226:Parabens / Q000302:isolation & purification; D012459:Salicylates / Q000302:isolation & purification; D020156:Salicylic Acid
D002109:Caffeic Acids; D062385:Hydroxybenzoates; D010226:Parabens; D012459:Salicylates; C009091:protocatechuic acid; C038193:4-hydroxybenzoic acid; D020156:Salicylic Acid; C040048:caffeic acid