Acta Chimica Sinica ›› 2006, Vol. 64 ›› Issue (10): 1043-1050. Previous Articles     Next Articles

Original Articles

电拓扑状态预测有机磷酸酯类化合物的气相色谱保留指数

王宇1,刘树深1,2,*,赵劲松1,王晓栋1,王连生1   

  1. (1南京大学环境学院 污染控制与资源化研究国家重点实验室 南京210093)
    (2同济大学环境学院 长江水环境教育部重点实验室 上海 200092)
  • 投稿日期:2005-07-08 修回日期:2006-01-25 发布日期:2006-05-25
  • 通讯作者: 刘树深

Prediction of Gas Chromatographic Retention Indices of Organophosphates by Electrotopological State Index

WANG Yu1, LIU Shu-Shen*,1,2, ZHAO Jin-Song1, WANG Xiao-Dong1, WANG Lian-Sheng1   

  1. (1 State Key Laboratory of Pollution Control and Resources Reuse, School of Environment, Nanjing University,
    Nanjing 210093)
    (2 Key Laboratory of Yangtze Aquatic Environment, Ministry of Education, College of Environmental Science and
    Engineering, Tongji University, Shanghai 200092)
  • Received:2005-07-08 Revised:2006-01-25 Published:2006-05-25
  • Contact: LIU Shu-Shen

Electrotopological state index (ETSI) for atom types was used to describe the structures of 35 organophosphates and a quantitative linear relationship between the ETSI descriptors and gas chromatographic retention indices (RI) was developed using the variable selection and modeling based on prediction (VSMP). It was found that some main structural factors influencing the RI of organophosphates are 7 substructures such as =CH2, ≡C—, aaC— (where “a” refers to a chemical bond in the aromatic ring), =O, O, Cl and Br, which were related to the molecular skeleton of organophosphates, substituent groups on phenyl ring, and alkyls binding to the bond of P—O. Three best 7-variable models, with the calibrated correlation coefficient of r>0.99 and the validated correlation coefficient of q>0.98 for three stationary phases, were built by multiple linear regression, which shows a good estimation ability and stability of models. A prediction power for the external samples was validated by the model built from the training set with 28 organophosphates.

Key words: electrotopological state index, organophosphate, quantitative structure-retention relationship, variable selection and modeling based on prediction