+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes



Artificial neural network modeling of Kováts retention indices for noncyclic and monocyclic terpenes



Journal of Chromatography. a 915(1-2): 177-183



A quantitative structure-property relationship study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques was carried out to investigate the retention behavior of some terpenes on the polar stationary phase (Carbowax 20 M). A collection of 53 noncyclic and monocyclic terpenes was chosen as data set that was randomly divided into two groups, a training set and a prediction set consist of 41 and 12 molecules, respectively. A total of six descriptors appearing in the MLR model consist of one electronic, two geometric, two topological and one physicochemical descriptors. Except for the geometric parameters the remaining descriptors have a pronounced effect on the retention behavior of the terpenes. A 6-5-1 ANN was generated by using the six descriptors appearing in the MLR model as inputs. The mean of relative errors between the ANN calculated and the experimental values of the Kováts retention indexs for the prediction set was 1.88%. This is in aggreement with the relative error obtained by experiment.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 045305355

Download citation: RISBibTeXText

PMID: 11358247

DOI: 10.1016/s0021-9673(00)01274-7


Related references

Simultaneous modeling of the Kovats retention indices on OV-1 and SE-54 stationary phases using artificial neural networks. Journal of Chromatography. a 955(2): 273-280, 2002

Prediction of Kovats retention indices of some aliphatic aldehydes and ketones on some stationary phases at different temperatures using artificial neural network. Journal of Chromatographic Science 46(5): 406-412, 2008

Atom-Type-Based AI Topological Indices for Artificial Neural Network Modeling of Retention Indices of Monomethylalkanes. Journal of Chromatographic Science 57(1): 1-8, 2019

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

Simultaneous modeling of the kovats retention indices on phenyl OV stationary phases with different polarity using MLR and ANN. QSAR and Combinatorial Science 25(10): 836-845, 2006

Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network. Journal of Chromatography. a 1108(2): 279-284, 2006

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography. Talanta 64(3): 785-790, 2004

Artificial neural network modeling of process and product indices in deep bed drying of rough rice. Turkish Journal of Agriculture and Forestry 36(6): 738-748, 2012

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices. Journal of Chromatography. a 1420: 98, 2015

Relationship between Kovats retention indices and molecular connectivity indices of tetralones, coumarins and structurally related compounds. Journal of Chromatography 630(1-2): 251-256, 1993

Development of comprehensive descriptors for multiple linear regression and artificial neural network modeling of retention behaviors of a variety of compounds on different stationary phases. Journal of Chromatography. a 903(1-2): 145-154, 2000

Retention prediction modeling of ginsenosides on a polyvinyl alcohol-bonded stationary phase at subambient temperatures using multiple linear regression and artificial neural network. Analytical Sciences 24(1): 139-148, 2008

Comparison of single best artificial neural network and neural network ensemble in modeling of palladium microextraction. Monatshefte für Chemie - Chemical Monthly 146(8): 1217-1227, 2015

Estimation of Kováts retention indices using group contributions. Journal of Chemical Information and Modeling 47(3): 975-980, 2007

The Application of an Artificial Neural Network (ANN) and a Genetic Programming Neural Network (GPNN) for the Modeling of Experimental Data of Slim Tube Permeability Reduction by Asphaltene Precipitation in Iranian Crude Oil Reservoirs. Petroleum Science and Technology 30(23): 2450-2459, 2012