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| 基团贡献法对取代苯类化合物生物降解性的预测 | |
| 引用本文： | 陆光华,赵元慧,汤洁,包国章.基团贡献法对取代苯类化合物生物降解性的预测[J].环境科学学报,2002,22(1):117-119. |
| 作者姓名： | 陆光华 赵元慧 汤洁 包国章 |
| 作者单位： | 1. 河海大学水文水资源及环境学院,南京,210098 2. 东北师范大学环境科学系,长春,130024 3. 吉林大学环境与资源学院,长春,130026 |
| 基金项目： | 国家自然科学基金 (2 98770 0 4)资助项目 |
| 摘 要： | 测定了 47种取代苯类化合物在松花江水中的 5日生化需氧量 (BOD5) .分别采用线性基团贡献法和非线性基团贡献法(人工神经网络法 )对化合物的生物降解性BOD5 ThOD(ThOD :理论需氧量 )进行QSBR研究 .得到不同基团对生物降解性的贡献为 :C6H5>COOH >OH >CH3 O CH3 >NH2 >Cl >NO2 .线性基团贡献法对于训练组和测试组的定性预测正确率分别为72 %和 86 % ;而人工神经网络法的预测正确率分别为 92 %和 86 % .预测结果表明线性和非线性基团贡献法的预测效果都很好 ,相比而言 ,非线性方法对生物降解性的预测更准确 |
| 关 键 词： | 生物降解 基因贡献法 神经网络 预测 取代苯类化合物 松花江 有机污染物 河流污染 |
| 文章编号： | 0253-2468(2002)-01-0117-03 |
| 收稿时间： | 2001/2/12 0:00:00 |
| 修稿时间： | 2001年2月12日 |
Prediction of biodegradation of substituted benzenes using group contribution method |
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| LU Guanghu,ZHAO Yuanhui,TANG Jie and BAO Guozhang.Prediction of biodegradation of substituted benzenes using group contribution method[J].Acta Scientiae Circumstantiae,2002,22(1):117-119. | |
| Authors: | LU Guanghu ZHAO Yuanhui TANG Jie and BAO Guozhang |
| Institution: | College of Hydrology,Water Resource and Environmental,Hohai University,Nanjing 210098,Department of Environmental Science,Northeast Normal University,Changchun 130024,College of Environment and Resource,Jilin University,Changchun 130026 and College of Environment and Resource,Jilin University,Changchun 130026 |
| Abstract: | The biochemical oxygen demand (BOD 5) of 47 substituted benzenes were determined. The quantitative structure biodegradability relationship studies were performed with biodegradability (BOD 5/ThOD) by the linear group contribution method and artificial neural network approach, respectively. The contribution order of various group to biodegradation is C 6H 5>COOH>OH>CH 3O/CH 3>NH 2>Cl>NO 2. The correct prediction rate of the linear group contribution method is 72% for the training set and 86% for the testing set, while it is 92% and 86%, respectively, when by neural network approach. It has been shown that both linear and nonlinear group contribution method are able to fit very well for either set. However, nonlinear group contribution method can provide a superior fit to biodegradation and produce a lower prediction error than the linear group contribution method. |
| Keywords: | biodegradation group neural network prediction |
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