In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a ...In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.展开更多
基金Supported by Shanghai Education Committee Project (No. 11YZ224)Shanghai Leading Academic Discipline Project (No. J51503)
文摘In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.
