Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods
A quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. T...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Chemistry |
| Online Access: | http://dx.doi.org/10.1155/2013/908586 |
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| author | Elaheh Konoz Amir H. M. Sarrafi Alireza Feizbakhsh Zahra Dashtbozorgi |
| author_facet | Elaheh Konoz Amir H. M. Sarrafi Alireza Feizbakhsh Zahra Dashtbozorgi |
| author_sort | Elaheh Konoz |
| collection | DOAJ |
| description | A quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. The six molecular descriptors that were considered to account for the effect of molecular structure on the retention time are number of nitrogen, Solvation connectivity index-chi 1, Balaban Y index, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted. A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. The standard error values of ANN model for training, test, and validation sets are 1.559, 1.517, and 1.249, respectively, which are less than those obtained reveals by multiple linear regressions model (2.402, 1.858, and 2.036, resp.). Results obtained the reliability and good predictability of nonlinear QSRR model to predict the retention time of pesticides. |
| format | Article |
| id | doaj-art-5b1a12a392fb4643ac674396f551b992 |
| institution | Kabale University |
| issn | 2090-9063 2090-9071 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
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| series | Journal of Chemistry |
| spelling | doaj-art-5b1a12a392fb4643ac674396f551b9922025-02-03T01:10:56ZengWileyJournal of Chemistry2090-90632090-90712013-01-01201310.1155/2013/908586908586Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics MethodsElaheh Konoz0Amir H. M. Sarrafi1Alireza Feizbakhsh2Zahra Dashtbozorgi3Department of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran 13185-768, IranDepartment of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran 13185-768, IranDepartment of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran 13185-768, IranDepartment of Chemistry, Islamic Azad University, Central Tehran Branch, Tehran 13185-768, IranA quantitative structure-retention relationships (QSRRs) method is employed to predict the retention time of 300 pesticide residues in animal tissues separated by gas chromatography-mass spectroscopy (GC-MS). Firstly, a six-parameter QSRR model was developed by means of multiple linear regression. The six molecular descriptors that were considered to account for the effect of molecular structure on the retention time are number of nitrogen, Solvation connectivity index-chi 1, Balaban Y index, Moran autocorrelation-lag 2/weighted by atomic Sanderson electronegativity, total absolute charge, and radial distribution function-6.0/unweighted. A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. The standard error values of ANN model for training, test, and validation sets are 1.559, 1.517, and 1.249, respectively, which are less than those obtained reveals by multiple linear regressions model (2.402, 1.858, and 2.036, resp.). Results obtained the reliability and good predictability of nonlinear QSRR model to predict the retention time of pesticides.http://dx.doi.org/10.1155/2013/908586 |
| spellingShingle | Elaheh Konoz Amir H. M. Sarrafi Alireza Feizbakhsh Zahra Dashtbozorgi Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods Journal of Chemistry |
| title | Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods |
| title_full | Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods |
| title_fullStr | Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods |
| title_full_unstemmed | Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods |
| title_short | Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods |
| title_sort | prediction of gas chromatography mass spectrometry retention times of pesticide residues by chemometrics methods |
| url | http://dx.doi.org/10.1155/2013/908586 |
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