Abstract
The quantitative structure-retention relationship (QSRR) was used for the prediction of retention indices of compounds in gas chromatography. 252 compounds containing boiling points (BP) was extracted from Molecular Operating Environment (MOE) database. After calculation of molecular descriptors of all compounds, genetic algorithm (GA) was used to select an optimal subset of the molecular descriptors. We investigated the predictive performance of four methods: GA on MLR (GA-MLR), the subset selected by GA-MLR was used to train SVR (GA-MLR-SVR), GA on SVR (GA-SVR) and GA on SVR with optimizing parameters (GA-SVR-Para). Twenty in-silicon experiments were conducted on each method. The experimental results show that the GA-SVR and GA-SVR-Para have better predictive performance with small variations. Among these four QSRR models, GA-SVR-Para achieved the best performance with a R 2 > 0.98.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kováts, E.: Gas-chromatographische Charakterisierung organischer Verbindungen. Teil 1: Retentionsindices aliphatischer Halogenide, Alkohole, Aldehyde und Ketone. Helvetica Chimica Acta 41(7), 1915–1932 (1958)
Van Den Dool, H.K., Dec, P.: A generalization of the retention index system including linear temperature programmed gas-liquid partition chromatography. Journal of Chromatography 11, 463–471 (1963)
Heberger, K.: Quantitative structure-(chromatographic) retention relationships. Journal of Chromatography A 1158(1-2), 273–305 (2007)
Hemmateenejad, B., Javadnia, K., Elyasi, M.: Quantitative structure-retention relationship for the Kovats retention indices of a large set of terpenes: A combined data splitting-feature selection strategy. Analytica Chimica Acta 592(1), 72–81 (2007)
Hu, R.J., et al.: QSPR prediction of GC retention indices for nitrogen-containing polycyclic aromatic compounds from heuristically computed molecular descriptors. Talanta 68(1), 31–39 (2005)
Nord, L.I., Fransson, D., Jacobsson, S.P.: Prediction of liquid chromatographic retention times of steroids by three-dimensional structure descriptors and partial least squares modeling. Chemometrics and Intelligent Laboratory Systems 44(1-2), 257–269 (1998)
Loukas, Y.L.: Artificial neural networks in liquid chromatography: efficient and improved quantitative structure-retention relationship models. Journal of Chromatography A 904(2), 119–129 (2000)
Jalali-Heravi, M., Fatemi, M.H.: Artificial neural network modeling of Kovats retention indices for noncyclic and monocyclic terpenes. Journal of Chromatography A 915(1-2), 177–183 (2001)
Luan, F., et al.: Prediction of retention time of a variety of volatile organic compounds based on the heuristic method and support vector machine. Analytica Chimica Acta 537(1-2), 101–110 (2005)
Hancock, T., et al.: A performance comparison of modem statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemometrics and Intelligent Laboratory Systems 76(2), 185–196 (2005)
Mihaleva, V.V., et al.: Automated procedure for candidate compound selection in GC-MS metabolomics based on prediction of Kovats retention index. Bioinformatics 25(6), 787–794 (2009)
Ustun, B., et al.: Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Analytica Chimica Acta 544(1-2), 292–305 (2005)
Eckel, W.P., Kind, T.: Use of boiling point-Lee retention index correlation for rapid review of gas chromatography-mass spectrometry data. Analytica Chimica Acta 494(1-2), 235–243 (2003)
MOE (The Molecular Operating Environment), Version (2008), Chemical Computing Group Inc., http://www.chemcomp.com
Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)
Scholkopf, B., et al.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing 45(11), 2758–2765 (1997)
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14(3), 199–222 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, J., Wang, B., Zhang, X. (2010). Optimal Selection of Support Vector Regression Parameters and Molecular Descriptors for Retention Indices Prediction. In: Huang, DS., Zhang, X., Reyes GarcÃa, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-14932-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14931-3
Online ISBN: 978-3-642-14932-0
eBook Packages: Computer ScienceComputer Science (R0)