th  - 1988 - 51st ENC Conference

TUE 9:10
MULTIVARIATE TECHNIQUES FOR ENHANCElVlENT
OF TWO DIMENSIONAL NMR SPECTRA
Hans Grahn, Frank Delaglio °, Mark W. Roggenbuck and George C. Levy
NMR and Data Processing Laboratory, NIH Resource and CASE Center,
Syracuse University, Syracuse 13244-1200.
By using multivariate representations of 2D NMR spectra, we show that systematic noise
such as tl and t2 ridges can be modeled by a Principal Component Analysis (PCA) method.
Later these noise models can be subtracted from the original data without distorting the
spectral features.
In addition, PCA can generate reconstructions of 2D spectra, which are solely based on the
systematic information from the data, and thus exclude random noise. Special data
transformations can be applied in conjunction with PCA in order to emphasize or reduce
specific features; this approach is employed in a diagonal suppression scheme for 2D NOE
spectra. All of these methods can be combined to optimize data in preparation for
automated, multivariate-based spectral analysis procedures, which benefit greatly from such
improvements.
34