Spectroscopic identification and quantitative analysis of binary mixtures using artificial neural networks.

This work deals with the application of artificial neural networks to two common problems in spectroscopy: the identification of distorted UV-visible spectra of a specific class of organic compounds, and the quantitative determination of single components in binary mixtures of these compounds. The examined species were six organic indicators, whose spectra are very similar to each other; the trained networks have proven to be very powerful in both applications.