Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.302
A series of hydrocarbons in FCC gasoline have been used to develop quantitative structure-retention relationships (QSRR) for their gas chromatographic retention index (RI) by using molecular descriptors which were calculated by Dragon software. QSRR models were built by adopting Multiple Linear Regression (MLR) and Artificial Neural Network (ANN). However, the results showed more or less the same quality with the predictive correlation coefficient R of 0.9952 and 0.9953 for MLR and ANN respectively. The obtained results told us that linear method is good enough to model the gas chromatographic retention index at least to the current dataset.
quantitative structure-retention relationships (QSRR), retention index (RI), Multiple Linear Regression (MLR), Artificial Neural Network (ANN)
Ling Ding, Xiaotong Zhang, Zhaolin Sun, Lijuan Song, Ting Sun, "Prediction of Gas Chromatographic Retention Index for Hydrocarbons in FCC Gasoline", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 651-655, doi:10.1109/CSIE.2009.302