Document Detail


Quantitative structure-property relationship study of retention time of some pesticides in gas chromatography.
MedLine Citation:
PMID:  17725865     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
A quantitative structure-property relationship (QSPR) study based on multiple linear regression (MLR) and artificial neural network (ANN) techniques is carried out to investigate the retention time behavior of some pesticides on the DB-5ms fused-silica column in gas chromatography. Five descriptors selected in the MLR model are: first component WHIM index (E1v), highest eigenvalue n.7 of burden matrix / weighted by atomic van der waals volume (BEHv7); average connectivity index Chi-2 (X2a), 3D-MoRSE signal 23 weighted by atomic Sanderson electronegativity (MoR23m); and principal moments of inertia B (PMIB). A 5-5-1 ANN is also generated to investigate the retention behavior of described pesticides using the same descriptors MLR model as inputs. The statistical parameters derived from MLR and ANN for all molecules are: correlation coefficient (R)(MLR) = 0.929, standard errors (SE)(MLR) = 3.452, R(ANN) = 0.943, and SE(ANN) = 3.112. The mean of relative errors between the MLR and ANN calculated and the experimental values of the retention times for the prediction set are 13.8% and 9.04%, respectively. The correlation coefficient and standard error of ANN model compared with MLR models showed the superiority of ANNs over regression models. This is partly due to the fact that ANN considers the interaction between different parameters as well as nonlinear relation.
Authors:
M R Hadjmohammadi; M H Fatemi; K Kamel
Related Documents :
15669705 - Prediction of mammalian toxicity of organophosphorus pesticides from qstr modeling.
11837705 - Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent ...
19038475 - Study on cad&rp for removable complete denture.
10430425 - Artificial neural networks improve the prediction of mortality in intracerebral hemorrh...
24074145 - Mesoscale climatic simulation of surface air temperature cooling by highly reflective g...
21492475 - Spatio-temporal analysis of malaria incidence at the village level in a malaria-endemic...
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Journal of chromatographic science     Volume:  45     ISSN:  0021-9665     ISO Abbreviation:  J Chromatogr Sci     Publication Date:  2007 Aug 
Date Detail:
Created Date:  2007-08-29     Completed Date:  2007-10-01     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0173225     Medline TA:  J Chromatogr Sci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  400-4     Citation Subset:  IM    
Affiliation:
Department of Chemistry, University of Mazandaran, P.O. Box 453, Babolsar, Iran.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Chromatography, Gas / methods*
Pesticides / chemistry*
Quantitative Structure-Activity Relationship
Reference Standards
Chemical
Reg. No./Substance:
0/Pesticides

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Multiresidue determination of 77 pesticides in textiles by gas chromatography-mass spectrometry.
Next Document:  Determination of sodium monofluoroacetate (1080) in biological samples as its 4-bromomethyl-7-methox...