<title>Use of a neural network for the analysis of fluorescence spectra from mixtures of polycyclic aromatic hydrocarbons</title>

Abstract

The use of a software implemented backpropagation neural network is reported for the qualitative and quantitative analysis of the fluorescence emission spectra from multicomponent mixtures of polycyclic aromatic hydrocarbons (PAHs) in solution. Analysis of two types of data is described. First, a backpropagation network is developed to determine the component concentrations in a ternary mixture of PAHs. The input data provided to the network consists of sampled two-dimensional (intensity vs. emission wavelength) fluorescence spectra. A second backpropagation network is investigated for the analysis of three-dimensional time resolved fluorescence emission spectra for a binary PAH mixture. Both of the networks are trained to recognize preselected compounds. Each trained network is then used to evaluate unknown emission spectra and to determine the presence and relative concentration of the compounds it has learned to recognize. Results from analysis of two-dimensional emission spectra show that the trained network was able to successfully identify the individual components and their concentrations in solutions containing mixtures of anthracene, chrysene, and acenapthene. Analysis of three-dimensional time resolved fluorescence emission data showed that individual components could be resolved in mixtures of two spectrally similar components (anthracene and chrysene). Although a network could also be trained to recognize anthracene and chrysene in binary mixtures using their two-dimensional emission spectra, use of three-dimensional time decay spectra reduced the learning time required to train the network by a factor of three.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.