Routine analysis of NMR data involves peak picking and integration to get chemical shifts (and couplings) and quantitative information (e.g. number of protons). When the peaks are not well resolved, none of these parameters can be accurately estimated and nonlinear least squares fit (curve fitting or deconvolution) is often performed to extract the desired information. However, deconvolution presents, at least two important difficulties:
In general, line fitting is applied to some limited number of lines in a spectrum as a deconvolution of the full spectrum is very difficult to say the least. This implies a manual intervention of the User (choice of multiplet, specification of the number of lines and of their starting parameters).Problem #2
Curve fitting requires the definition of an analytical model for the line shape and in particular, NMR lineshaphes have typically been assumed to be either Lorentzian, Gaussian, or a combination of both (e.g. Voight Profile). The problem is that Lorentzian deconvolutions are numerically ill defined because all complete sets of Lorentzian-shaped functions are approximately linearly dependent (in other words, a Lorentzian peak can be approximated very well by several Lorentzian lines). This problem is specially important in 1H-NMR spectra where peaks are really complicated envelopes of many unresolved transitions (for example, in a generic 10 spin system there are 5120 distinct main transitions, but one typically resolves less than 100 peaks).
These problems have been the motivation of the development of a brand new peak analysis algorithm, the so-called GSD (Global Spectral Deconvolution) which has been recently presented by Stan Sykora in a talk he gave at MMCE 2009 conference. In fact, GSD is now fully operative within MestReNova .
If you are interested in GSD and planning to visit ENC, we will be pleased to show you every detail at the user meeting we will keep on Sunday 29th March and at our exhibitor and hospitality suite (you do not need to be a MestReNova User to participate).