th  - 1987 - 51st ENC Conference

WK30
PATTERN RECOGNITION IN 2D NMR.
A MULTIVARIATE STATISTICAL APPROACH WITH LOGIC PROGRAMMING.
H. Grahn , F. Delaglio, M. A. Delsuc and G. C. Levy,
NMR and Data Processing Laboratory, Bowne Hall,
Syracuse University, Syracuse, NY 13244-1200
A new method for the analysis of two-dimensional NMR data
is presented. The method, based on principal component
analysis (PCA), is shown to be very efficient in the
modeling of different spin patterns in homonuclear shift
correlation 2D spectra.
The PCA method can be described as a graphical method
which projects multidimensional data down on a few
dimensional space (line, plane or hyperplane) and hence
provides a simplified interpretation of the clusters in an
n-dimensional space. The scope of the method is that similar
objects (spins) can be grouped together. Based on a prior
knowledge of different spin systems, new "unknown" compounds
can be classified and the spin connectivities in the molecule
can be outlined.
An important difference to other pattern recognition
methods is that no previous assumptions about the analyzed
data are needed, e.g., coupling constant information. The
method uses the intensities of the cross-peaks and the
chemical shifts of the diagonal peaks as parameters. The
first step in analysis is the preprocessing of the data. This
step involves locating all peaks in the data and scaling peak
intensities to unit variance. The peak data are reduced to a
system of objects and variables for the PC analysis. In the
course of the PC analysis, n-dimensional projections of the
data are constructed which contain patterns which will group
all the objects comprising each spin system into a
characteristic pattern. Using the logic programming language
PROLOG, the patterns can be located and the corresponding
spin systems identified.
Using PC analysis in combination with logic programming,
it is possible to identify the different spin systems in a
spectrum having mixed spin systems.
We acknowledge NIH Grant RR-01317, N.A.T.O. for support of
Dr. Delsuc and Troedsson Research Fund, Sweden, for support
of Dr. H. Grahn.