Automatic reduction of NMR spectroscopic data for statistical and pattern recognition classification of samples.

A general method of automatically reducing NMR spectra to provide numerical descriptors of samples has been developed and investigated. These descriptors can be used as input to pattern recognition or multivariate algorithms for sample classification. The methods have been tested using 600 MHz one-dimensional 1H NMR spectra of biofluids which are complex mixtures. The approach is, in principle, applicable to multidimensional and heteronuclear NMR spectra and to other types of liquid samples such as oils and foodstuffs as well as to situations such as 1H or 31P NMR in vivo and solid state NMR in drug formulation analysis. The method relies upon apportioning the information in the spectra to individual contiguous segments and allowing specified regions of the spectra to be omitted. Three approaches, based on the number of peaks, the summed peak heights and the summed peak areas respectively in each segment, have been tested. The effect of segment width and overlap and the effects of manipulation of the NMR spectra have been evaluated in terms of the classification of the samples using principal components analysis. A simple method of generating NMR based spectral descriptors for object classification is thus proposed.