Application of neural networks for response surface modeling in HPLC optimization

Agatonović-Kuštrin, Snežana; Zečević, Mira; Živanović, Ljubiša; Tucker, Ian G.

doi:10.1016/s0003-2670(98)00121-4

Cited by 64 publications

(25 citation statements)

References 8 publications

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“…Artificial neural networks (ANNs) have been applied to a wide variety of chemical problems, such as simulation of mass spectra [15], modeling of ion interaction chromatography [16,17], prediction of 13 C NMR chemical shift [18], response surface modeling in HPLC optimization [19], and quantitative structure-activity relationship (QSAR) studies [20][21][22]. The flame ionization detector and thermal conductivity detector response factors for a diverse set of organic molecules were also predicted using ANN models [23,24].…”

Section: K'mentioning

confidence: 99%

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Fatemi

Baher

2005

J Anal Chem

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In this study, a quantitative structure-property relationship technique has been used for the prediction of retention factors for some organic compounds in supercritical fluid chromatography using an artificial neural network. The best descriptors that appear in this model are the number of single bonds, the number of double bonds, and the hydrophilic factor. These descriptors were used as inputs for a generated artificial neural network. This network has a 3 : 3 : 1 topology that was trained using a back-propagation algorithm. The crossvalidation method was used to evaluate the predictive power of the generated network. The results obtained by artificial neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression technique. Comparison of these results shows the ability of the artificial neural network model to predict retention factors.

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Section: K'mentioning

confidence: 99%

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Fatemi

Baher

2005

J Anal Chem

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“…ANNs have been used to predict analyte retention times or resolutions in a number of isocratic HPLC separations [4][5][6][7][8][9][10], however their use in optimising gradient elution separations has been limited. Madden et al [11] used an ANN to predict anion retention times in ion chromatography.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of HPLC gradient separations using artificial neural networks (ANNs): Application to benzodiazepines in post-mortem samples

Webb¹,

Doble²,

Dawson³

2009

Journal of Chromatography B

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“…After calculation of the molecular descriptors, linear method such as multiple linear regression (MLR), or nonlinear method such as artificial neural network (ANN) can be employed to derive correlation between the molecular structures and retention parameters. ANNs have been applied to a wide variety of chemical problems, such as simulation of mass spectra [26], modeling of ion interaction chromatography [27], prediction of 13 C-NMR chemical shift [28], response surface modeling in HPLC optimization [29], and quantitative structure-activity relationship studies [30 -32]. The flame ionization detector and thermal conductivity detector response factors for a diverse set of organic molecules were also predicted by using the ANN method [33,34].…”

Section: Introductionmentioning

confidence: 99%

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

Fatemi

Malekzadeh

Shamseddin

2009

J of Separation Science

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In this work quantitative structure-retention relationship models were developed to predict the solute retention factors in supercritical fluid chromatography (SFC) in various organic modifiers. Data set contains the retention data of 35 various organic compounds in 0, 2, 4 and 6% of methanol in mobile phase. The obtained 140 data points were divided into training, internal and external test sets which have 93, 23 and 24 retention data. The diversity validation test was performed on data the set to ensure that the structure of the training and/or test sets can represent those of the whole ones. Descriptors which were selected by stepwise multiple linear regression (MLR) methods are: the percent of organic modifier in mobile phase, salvation connectivity index chi-2, salvation connectivity index chi-5, H attached to heteroatom, the 2(nd) structural information content and polarity parameter. These descriptors were used as features in generation of linear and non-linear models using MLR and artificial neural network (ANN) methods, respectively. The root mean square error of MLR model are 0.116, 0.138 and 0.260 for training, internal and external test sets, respectively, while these values are 0.036, 0.097 and 0.244 for ANN model, respectively. Comparison between these values and other statistical parameters obtained from these two models reveals the credibility of ANN in prediction of solute retention factors in SFC.

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Application of neural networks for response surface modeling in HPLC optimization

Cited by 64 publications

References 8 publications

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Prediction of Retention Factors in Supercritical Fluid Chromatography Using Artificial Neural Network

Optimisation of HPLC gradient separations using artificial neural networks (ANNs): Application to benzodiazepines in post-mortem samples

Prediction of supercritical fluid chromatographic retention factors at different percents of organic modifiers in mobile phase

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