Publication Date:
2011-03-03
Description:
A comparative quantitative structure–retention relationship (QSRR) study has been carried out to predict the retention time of nitrobenzene derivatives using original molecular descriptors and multivariate image analysis (MIA) descriptors. First, original molecular descriptors were generated from molecular structures and applied to construct QSRR models using multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) modeling methods. Then, multivariate image analysis (MIA) descriptors were generated from pixels of images and analyzed using correlation ranking–principal component regression (CR–PCR) and correlation ranking–principal component–artificial neural network (CR–PC–ANN) methods. In this paper, the CR–PC–ANN method presented better results than the other methods for predicting the retention time of the studied compounds. Coefficients of determination ( R 2 ) using the CR–PC–ANN method for the training, test, and validation sets were 0.989, 0.999, and 0.999, respectively. Content Type Journal Article Pages 1-10 DOI 10.1007/s10337-011-1969-7 Authors Zahra Garkani-Nejad, Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran Mohammad Ahmadvand, Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran Journal Chromatographia Online ISSN 1612-1112 Print ISSN 0009-5893
Print ISSN:
0009-5893
Electronic ISSN:
1612-1112
Topics:
Chemistry and Pharmacology
