Two applications are considered, the determina tion of presence and concentration of organic gases and solvants and the prediction of corrosion resistance of car body steels. Both applications use a similar method based on optical measurement. The goal is to extract correlations of the spectral data and to classify unknown spectra. Evaluation of spectral data with a lot of components is an important problem in physics and chemistry.
Traditional methods fail because of noisy and complex data.
In this work neural networks are combined with traditional technics. Here data analysis with Kohonen's self-organizing map is presented. Different configurations are used to show capacities of self-organizing maps. A network architecture is developed, which turned out to be suitable for the detection of gas concentrations. Traditional tools of signal processing help to improve the quality of input data.