Quantitative Structure-biodegradability Relationship Study about the Aerobic Biodegradation of Some Aromatic Compounds  

作　　者：荆国华 李小林 周作明 

机构地区：[1]Department of Environmental Science & Engineering,Huaqiao University

出　　处：《Chinese Journal of Structural Chemistry》2011年第3期368-375,共8页结构化学（英文）

基　　金：supported by the Natural Science Foundation of Fujian Province (D0710019);the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (09QZR07)

摘　　要：10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships （QSBR） studies were performed by the multiple linear regression （MLR）, principal component regression （PCR） and back propagation artificial neural network （BP-ANN）, respectively. The root mean square error （RMSE） of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors （MAPE） are 0.1638 and 0.0326, the squared correlation coefficients （R^2） are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.

关 键 词：aromatic compounds quantitative structure-biodegradability relationships multiple linear regression principal component regression artificial neural network 

分 类 号：X172[环境科学与工程—环境科学]