Abstract
A quantitative structure-property relationship (QSPR) study based on the artificial neural network (ANN) technique was performed for the prediction of gas chromatography retention indexes of methyl-substituted alkanes produced by insects. Simple descriptors such as the total number of carbons in the backbone, the number of the multiple methyl groups attached to the carbon chain, and their relative positions were selected, and an ANN with a 9 : 8 : 1 architecture was generated using the nine descriptors in the input layer. The average relative error was 3.3%. The method was also compared with the QSPR method, which utilizes topological and quantum chemical descriptors.
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From Zhurnal Analiticheskoi Khimii, Vol. 60, No. 8, 2005, pp. 825–831.
Original English Text Copyright © 2005 by Zarei, Atabati.
The text was submitted by the authors in English.
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Zarei, K., Atabati, M. Prediction of GC Retention Indexes for Insect-Produced Methyl-Substituted Alkanes Using an Artificial Neural Network and Simple Structural Descriptors. J Anal Chem 60, 732–737 (2005). https://doi.org/10.1007/s10809-005-0172-0
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DOI: https://doi.org/10.1007/s10809-005-0172-0