Neural networks in analytical chemistry.

This chapter covers a part of the spectrum of neural-network uses in analytical chemistry. Different architectures of neural networks are described briefly. The chapter focuses on the development of three-layer artificial neural network for modeling the anti-HIV activity of the HETP derivatives and activity parameters (pIC50) of heparanase inhibitors. The use of a genetic algorithm-kernel partial least squares algorithm combined with an artificial neural network (GA-KPLS-ANN) is described for predicting the activities of a series of aromatic sulfonamides. The retention behavior of terpenes and volatile organic compounds and predicting the response surface of different detection systems are presented as typical applications of ANNs in chromatographic area. The use of ANNs is explored in electrophoresis with emphasizes on its application on peptide mapping. Simulation of the electropherogram of glucagons and horse cytochrome C is described as peptide models. This chapter also focuses on discussing the role of ANNs in the simulation of mass and 13C-NMR spectra for noncyclic alkenes and alkanes and lignin and xanthones, respectively.