COMPARING THE PERFORMANCE OF NEURAL NETWORKS TO WELL-ESTABLISHED METHODS OF MULTIVARIATE DATA-ANALYSIS - THE CLASSIFICATION OF MASS-SPECTRAL DATA

The performance of neural networks in classifying mass spectral data is evaluated and compared to methods of multivariate data analysis and pattern recognition. Back propagation networks are matched with linear discriminant analysis, Kohonen feature maps are compared to the k-nearest neighbour clustering algorithm. Eight classifiers were trained, in order to discriminate mass spectra of steroids from eight distinct classes of chemical compounds. The results obtained show slightly better performance of Kohonen networks compared to k-nearest neighbour clustering and equal performance of multi-layer perceptrons and discriminant analysis.