Even an ignorant can increase both resolution and sensitivity, simultaneously, thousands of times or more. It's enough to use a non-linear method. Suppose you have an ugly spectrum, like this:
I don't know what it is, it's the first spectrum I have found today. Suppose I know, instead, what it is. I may be certain that they are two triplets. I can perform peak-picking, retain the 6 highest points, then create a synthetic spectrum containing 6 perfect lorentzian shapes, with line width = 0.1 Hz and noise = 0. Maximum resolution and maximum signal/noise.
Non linear methods work like this. Either they filter out all signals below a given threshold, or they select the given number of highest signals. It's risky, unless you already know what to expect (if you know everything, however, there is no need to collect the spectrum). If you need elegant presentations, you can get them with software. Increased knowledge is a different thing.
What does not work in general, can often be the perfect solution in some special cases. At the beginning of the year I mentioned the Chenomx Suite of programs. They require reference deconvolution to be applied as a pre-treatment on every experimental spectrum. Reference Deconvolution is an ancient trick that never found widespread implementation. After years it has found a reason to exist. Linear Prediction is a technique that emerged in the 80s. Its purpose was to extract the NMR parameters from the FID avoiding both FT and spectroscopic analysis. Nobody was using it. After a decade it found a different application: it allowed shorter acquisition times for HSQC and similar experiments.
Research in the field of NMR processing is precious, but I don't expect it to change the whole field. It can find specific solutions to specific problems.
No comments:
Post a Comment