Neural network and experimental design to investigate the effect of five factors in ion-interaction high-performance liquid chromatography

The effect of five experimental parameters on the ion-interaction chromatographic retention of pesticides characterized by different polarity was investigated by means of experimental design and artificial neural network treatments. The factors considered were: (1) the mobile phase pH; (2) N, the alkyl-chain length of the IIR (ion-interaction reagent); (3) CM, the organic modifier concentration in the mobile phase (4) CR, the concentration of IIR and (5) F, the flow-rate. The use of fractional design and Hoke design allowed useful information to be drawn about the retention mechanism involved and to build, through artificial neural network treatment (ANN), a model characterised by both descriptive and predictive ability. Four neurons and a bias unit were employed. The ANN proved to be a useful instrument in the optimisation of the chromatographic separation, as regards resolution and total analysis time: the experimental retention obtained in the optimal conditions always differed within 14% from the predicted ones.

[1]  M. Gennaro,et al.  Separation of a Mixture of Hydrophilic and Hydrophobic Herbicides by Ion-Interaction Reversed-Phase HPLC , 1996 .

[2]  M. Gennaro,et al.  Retention dependence on organic modifier and interaction reagent concentration in reversed-phase ion-interaction HPLC , 1995 .

[3]  M. Gennaro,et al.  Retention of Anions as a Function of Mobile Phase PH in Ion-Interaction RP-HPLC , 1994 .

[4]  Dusan Stulik,et al.  Simple encoding of infrared spectra for pattern recognition Part 2. Neural network approach using back-propagation and associative Hopfield memory , 1995 .

[5]  A. Bos,et al.  Processing of signals from an ion-elective electrode array by a neural network , 1990 .

[6]  S. Ventura,et al.  Multicomponent kinetic determinations using artificial neural networks. , 1995, Analytical chemistry.

[7]  H. Metting,et al.  Neural networks in high-performance liquid chromatography optimization: response surface modeling. , 1996, Journal of chromatography. A.

[8]  D. A. Doornbos,et al.  PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION , 1993 .

[9]  M. Gennaro,et al.  Investigation by experimental design and regression models of the effect of five experimental factors on ion-interaction high-performance liquid chromatographic retention , 1996 .

[10]  M. Gennaro,et al.  The Role of pH of the Mobile-Phase in Ion-Interaction RP-HPLC , 1994 .

[11]  Y. Sohrin.  Determination of organometallic and inorganic germaniun by inductively coupled plasma atomic emission spectrometry , 1991 .

[12]  M. Bos,et al.  Comparison of the training of neural networks for quantitative x-ray fluorescence spectrometry by a genetic algorithm and backward error propagation , 1991 .

[13]  Jan St»hlberg.  The Gouy—Chapman theory in combination with a modified langmuir isotherm as a theoretical model for ion-pair chromatography , 1986 .

[14]  B. Bidlingmeyer.  Separation of Ionic Compounds by Reversed-Phase Liquid Chromatography An Update of Ion-Pairing Techniques , 1980 .

[15]  M. Gennaro,et al.  Determination of the principal anionic components in wines and soft drinks, by ion interaction reversed-phase high-performance liquid chromatography , 1989 .

[16]  J. Zupan,et al.  Neural Networks in Chemistry , 1993 .

[17]  R. M. Wharton,et al.  Confidence Bands for the Weibull Distribution , 1975 .

[18]  J. Zupan,et al.  Neural networks: A new method for solving chemical problems or just a passing phase? , 1991 .

[19]  M. Gennaro,et al.  Separation of aliphatic and aromatic amines by the ion interaction reagent RP-HPLC technique , 1988 .

[20]  S P Levine,et al.  Spectral peak verification and recognition using a multilayered neural network. , 1990, Analytical chemistry.