Department of Chemistry, Faculty of Sciences, Arak Branch, Islamic Azad University, Arak, Iran.
Environ Sci Pollut Res Int. 2012 May;19(4):1252-9. doi: 10.1007/s11356-011-0650-x. Epub 2011 Nov 11.
A quantitative structure-retention relation (QSRR) study was conducted on the retention times of organic pollutants in textile wastewaters and landfill leachate which was obtained by liquid chromatography-reversed phase-atmospheric pressure chemical ionization-mass spectrometry.
The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square) and nonlinear (Levenberg-Marquardt artificial neural network, L-M ANN) methods. Linear and nonlinear models provide good results whereas more accurate results were obtained by the L-M ANN model.
This is the first research on the QSRR of the organic pollutants in textile wastewaters and landfill leachate against the retention time.
通过液相色谱-反相-大气压化学电离-质谱法，对取自纺织废水和垃圾渗滤液的有机污染物的保留时间进行了定量结构-保留关系（QSRR）研究。
遗传算法被用作描述符选择和模型开发方法。通过线性（偏最小二乘）和非线性（列文伯格-马夸尔特人工神经网络，L-M ANN）方法，对所选分子描述符与保留时间之间的关系进行建模。线性和非线性模型均提供了良好的结果，而 L-M ANN 模型则提供了更准确的结果。
这是首次针对纺织废水和垃圾渗滤液中有机污染物的保留时间进行 QSRR 研究。
