CAJ | 학술논문

采用量子化学密度泛函B3LYP/6-311+G*，在高斯09软件上计算了32个多氯联苯类化合物的电子结构参数；筛选出影响化合物色谱保留时间显著的5个变量，并建立其结构与保留时间之间的定量关系（MLR 模型）；同时，利用人工神经网络(artificial neural network, ANN)法建立相应的QSRR模型（ANN模型）与之对比。所建MLR模型的相关系数R=0.904，标准误差Se=0.542；ANN模型的相关系数R=0.981，标准误差Se=0.213。表明所建立的QSRR模型的稳定性和预测能力良好。结果表明，多氯联苯化合物的色谱保留时间与前沿轨道能级差ΔE和分子最高占有轨道能EH成正比例关系。所建模型为预测多氯联苯化合物的色谱保留时间提供理论指导。
채용양자화학밀도범함B3LYP/6-311+G*，재고사09연건상계산료32개다록련분류화합물적전자결구삼수；사선출영향화합물색보보류시간현저적5개변량，병건립기결구여보류시간지간적정량관계（MLR 모형）；동시，이용인공신경망락(artificial neural network, ANN)법건립상응적QSRR모형（ANN모형）여지대비。소건MLR모형적상관계수R=0.904，표준오차Se=0.542；ANN모형적상관계수R=0.981，표준오차Se=0.213。표명소건립적QSRR모형적은정성화예측능력량호。결과표명，다록련분화합물적색보보류시간여전연궤도능급차ΔE화분자최고점유궤도능EH성정비례관계。소건모형위예측다록련분화합물적색보보류시간제공이론지도。
For 32 PCBs, quantum chemistry calculation of electronic properties were carried out at density functional theory (DFT) B3LYP/6-311+G*level by Gaussion09. 5 important parameters were selected and the quantitative structure retention relationship (QSRR) model was set up by multiple linear regressions (MLR) method. Furthermore, using artificial neural network (ANN), the QSRR model was obtained in order to make contrast. For the artificial neural network method, the correlation coefficient R=0.981 and the standard error Se=0.213, while for the multiple linear regression analysis R=0.904 and Se=0.542. These shows that the QSRR models have both favorable estimation stability and good prediction capability. This indicates that the retention time of PCBs and bothΔE and EH are in direct proportion. Successful QSRRs were developed to predict the retention time of PCBs.