Application of artificial neuralnetworks for solving pharmaceutical issues

Abstract

Nowadays, modern analytical instruments allow producing great amounts of information for a large number of samples (objects) that can be analyzed in relatively short time. This leads to the availability of multivariate data matrices that require the use of mathematical and statistical procedures, in order to efficiently extract the maximum useful information from data. When processing the data obtained as a result of the chromatographic analysis and various spectroscopic methods, as well as sensory systems, such as the electronic nose and electronic tongue, one cannot avoid applying modern chemometric methods, e.g., pattern recognition and classification algorithms, discriminative analysis, and artificial neural networks.