Published:Journal of Chromatographic Science,
ISSN 0021-9665 Volume
46, Number 5, May/June 2008, pp. 406-412
Prediction of Kovats Retention Indices of Some
Aliphatic Aldehydes and Ketones on Some Stationary Phases at
Different Temperatures Using Artificial Neural Network
Elaheh Konoz1, Mohammad H. Fatemi2,
and Razieh Faraji2
1Department of Chemistry, Central Tehran Branch, Islamic Azad
University, Tehran, Iran and
2Department of Chemistry, Mazandaran
University, Babolsar, Iran
In this work, the Kovats retention indices of
aliphatic ketones and aldehydes on four stationary phases at
different temperatures are predicted. The data set consists of
retention indices of 35 aldehydes and ketones on HP-1, HP-50,
DB-210, and HP-Innowax stationary phase. The molecular descriptors
that appear in this model are: path one connectivity index, fractional
atomic charge weighted by partial positive surface area, and
dipole moment, which are selected by stepwise multiple linear
regression (MLR). The selected descriptors encode steric and
electronic aspects of molecules. These descriptors, together
with column temperature, are used as inputs of the constructed
artificial neural network (ANN). The optimized network has 4-3-4
topology, in which its outputs are retention indices of molecules
at four stationary phases at the desired temperature. Comparison
between statistical results calculated for MLR and ANN models
reveals that all statistics have improved considerably in the
case of the ANN model. The improved statistics for the ANN would
suggest the existence of a nonlinear relation between selected
molecular descriptors and their retention in gas chromatography.
Also, the simultaneous prediction of retention indices for aldehydes
and ketones at four stationary phases at different temperatures
using only three molecular descriptors shows the capability of
the obtained ANN model.
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