基于亚结构分子片段的多氯联苯色谱相对保留时间计算模型

S. Saaidpour
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引用次数: 2

摘要

定量结构-保留关系(QSRR)分析是一种将色谱保留时间与溶质的化学结构联系起来的有用技术。利用直接由分子结构得到的亚结构分子片段(SMF),计算了209种多氯联苯(pcb)在SE-54固定相上的气相色谱相对保留时间(RRTs)。建立了8变量回归方程,相关系数为0.9945,均方根误差为0.0134。根据QSRR方法,将前向和后向逐步回归变量选择和多元线性回归分析(MLRA)相结合来描述分子结构对PCB板RRT的影响。为了定量地将RRT与分子结构联系起来,对ISIDA软件提供的163个亚结构分子片段(SMF)进行MLR分析。变量亚区选择选择的8个片段均属于亚片段,充分代表了分离过程中影响PCB对SE-54固定相亲和力的结构因素。最后,基于留一交叉验证选择了QSRR模型,并对42个排除在模型校准之外的代表性化合物进行了预测能力测试。MLR模型的预测结果与实验值吻合较好。应用MLR方法预测检验集的平方交叉验证相关系数(q2)为0.9913,均方根误差(RMSE)为0.0169。
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Computational Model For Chromatographic Relative Retention Time of Polychlorinated Biphenyls Using Sub-structural Molecular Fragments
Quantitative structure-retention relationship (QSRR) analysis is a useful technique capable of relating chromato- graphic retention time to the chemical structure of a solute. Using the sub-structural molecular fragments (SMF) derived directly from the molecular structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were calculated. An eight-variable regression equation with the correlation coefficient of 0.9945 and the root mean square errors of 0.0134 was developed. Forward and backward stepwise regression variable selection and multi-linear regression analysis (MLRA) are combined to describe the effect of molecular structure on the RRT of PCB according to the QSRR method. To quantitatively relate RRT with the molecular structure MLR analysis is performed on the set of 163 sub-structural molecular fragments (SMF) provided by the ISIDA software. The eight fragments selected by variable subset selection, all belonging to the sub-fragments, adequately represent the structural factors influencing the affinity of PCB to SE-54 stationary phase in the separation process. Finally, a QSRR model is selected based on leave-one-out cross-validation and its prediction ability is further tested on 42 representative compounds excluded from model calibration. The prediction results from the MLR model are in good agreement with the experimental values. By applying the MLR method we can predict the test set with squared cross validated correlation coefficient (Q 2) of 0.9913 and root mean square error (RMSE) of 0.0169.
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