caa a22 4500 445801476 CHVBK 20180317145142.0 cr unu---uuuuu 170323e20110401xx s 000 0 eng 10.1007/s10337-011-1969-7 doi (NATIONALLICENCE)springer-10.1007/s10337-011-1969-7 Comparative QSRR Modeling of Nitrobenzene Derivatives Based on Original Molecular Descriptors and Multivariate Image Analysis Descriptors [Elektronische Daten] [Zahra Garkani-Nejad, Mohammad Ahmadvand] 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. Springer-Verlag, 2011 Quantitative structure-retention relationship (QSRR) nationallicence Multivariate image analysis (MIA) nationallicence Principal component regression (PCR) nationallicence Artificial neural network (ANN) nationallicence Garkani-Nejad Zahra Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran aut Ahmadvand Mohammad Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran aut Chromatographia Springer-Verlag 73/7-8(2011-04-01), 733-742 0009-5893 73:7-8<733 2011 73 10337 https://doi.org/10.1007/s10337-011-1969-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10337-011-1969-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Garkani-Nejad Zahra Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran aut NATIONALLICENCE 700 1- Ahmadvand Mohammad Chemistry Department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran aut NATIONALLICENCE 773 0- Chromatographia Springer-Verlag 73/7-8(2011-04-01), 733-742 0009-5893 73:7-8<733 2011 73 10337 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer