Simultaneous determination of multicomponents in air toxic organic compounds using Artificial Neural Networks in FTIR spectroscopy

The application of Artificial Neural Networks (ANNs) for nonlinear multivar iate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using t he back-propagation learning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in or der to get a satisfactory combination of all parameters. The mixtures of fo ur air toxic organic compounds whose FTIR spectra are overlapped were chose n to evaluate the calibration and prediction ability of the network. The re lative standard error (RSD%), the percent standard error of prediction samp les (%SEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.