title: Quantification of two-dimensional NOE spectra via a combined linear and nonlinear least-squares fit.
abstract: Determining the volumes of peaks in 2D NMR spectra can be prohibitively difficult in cases of overlapping, broad lines. Deconvolution and parameter estimation can be attempted on either the time-domain or the frequency-domain data. We present a method of estimating spectral parameters from frequency-domain data, using a combination of Lorentzian and Gaussian lineshapes for reference lines. This approach combines a previously published method of projecting the data on a linear space spanned by reference lines with a nonlinear least-squares fitting algorithm. Comparison of this method with other published methods of frequency-domain deconvolution shows that it is both more precise and more accurate when estimating 2D volumes.
authors.affiliation: Department of Chemistry, Stanford University, CA 94305-5080.
authors.forename: J W
authors.initials: JW
authors.lastname: Brown
authors.forename: W H
authors.initials: WH
authors.lastname: Huestis
mesh_terms: D000465:Algorithms; D016018:Least-Squares Analysis; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D017711:Nonlinear Dynamics