The primary goal of spectra classification is to find correlation between the properties of compounds and their mass spectra. Because physical and chemical properties and biological activities of chemical compounds are to a large extent a function of molecular structure, the results of classification analysis reflect structural features that are determined by fragmentation ions appearing in a mass spectrum. From the user viewpoint the important advantage of classification methods is the fact that the user does not require detailed knowledge of the complex spectra-structure relationship to get satisfactory results. Classification strategy in Mass Frontier is based a user-friendly graphic presentation of the results, which can be easily viewed on the screen.

Mass Frontier offers the classification method called Principal Component Analysis (PCA). The central idea of principal component analysis is to reduce the dimensionality of a data set in which there are a large number of interrelated (i.e. correlated) variables, while retaining as much as possible of the variation present in the data set. In the case of mass spectrometry, the data set consists of the mass spectra of different compounds. The mass spectra are expressed as the intensities of individual m/z ratios (i.e. variables).

Self-Organizing Maps (SOM), sometimes called Kohonen networks, are a special class of neural networks. A self-organizing map consists of neurons placed at the nodes of a two-dimensional lattice. The neurons become selectively activated to various input mass spectra or classes of spectra in the course of a competitive learning process. The neurons compete among themselves to be activated or excluded. SOM can be considered as a nonlinear generalization of PCA.

Cluster analysis is a technique for grouping data into clusters to find common structural features in spectral data. Membership degrees between zero and one are used in fuzzy clustering instead of crisp assignments of the data to clusters. Fuzzy clustering is based on the dot product distance between the center of clusters and experimental spectral points. The dot product is calculated from mass spectral n-dimensional space with the intensities being the coordinates and m/z values dimensions. The dimensionality of spectral space is determined by the number of peaks whose intensities are above the predefined threshold. The number of clusters is usually defined a priori.

It has been shown that various mathematical transformations of mass spectra may increase classification efficiency. Better separation of classes can be achieved in some cases if transformed, instead of original spectra, are submitted to classification. In addition, some transformation procedures reduce the number of variables and lower the dimensionality of the spectral space, which shortens the computing time.