Prediction of the Lee retention indices of polycyclic aromatic hydrocarbons by artificial neural network

2006 ◽  
Vol 1108 (2) ◽  
pp. 279-284 ◽  
Author(s):  
Biljana Škrbić ◽  
Antonije Onjia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Retention Indices ◽  
Polycyclic Aromatic ◽  
Artificial Neural

scholarly journals Fluorescence optosensor using an artificial neural network for screening of polycyclic aromatic hydrocarbons

2004 ◽  
Vol 510 (2) ◽  
pp. 183-187 ◽  
Author(s):  
J.F. Fernández-Sánchez ◽  
A. Segura Carretero ◽  
J.M. Benı́tez-Sánchez ◽  
C. Cruces-Blanco ◽  
A. Fernández-Gutiérrez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Polycyclic Aromatic ◽  
Artificial Neural

scholarly journals Artificial neural network prediction of quantitative structure: Retention relationships of polycyclic aromatic hydocarbons in gas chromatography

2005 ◽  
Vol 70 (11) ◽  
pp. 1291-1300 ◽  
Author(s):  
Snezana Sremac ◽  
Biljana Skrbic ◽  
Antonije Onjia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Chromatography ◽  
Retention Indices ◽  
Retention Data ◽  
Ann Model ◽  
Linear Temperature ◽  
Unseen Data ◽  
Polycyclic Aromatic ◽  
Artificial Neural

A feed-forward artificial neural network (ANN) model was used to link molecular structures (boiling points, connectivity indices and molecular weights) and retention indices of polycyclic aromatic hydrocarbons (PAHs) in linear temperature- programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al. [Anal. Chem. 51 (1979) 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (+-3 %).

Detection of Polycyclic Aromatic Hydrocarbons Combining Fluorescence Analysis with ABC-BP Neural Network

2015 ◽  
Vol 42 (11) ◽  
pp. 1115001
Author(s):  
王书涛 Wang Shutao ◽  
陈东营 Chen Dongying ◽  
王兴龙 Wang Xinglong ◽  
韩欢欢 Han Huanhuan ◽  
王佳亮 Wang Jialiang
Keyword(s):  
Neural Network ◽  
Polycyclic Aromatic Hydrocarbons ◽  
Aromatic Hydrocarbons ◽  
Bp Neural Network ◽  
Fluorescence Analysis ◽  
Polycyclic Aromatic
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