NMR (Nuclear Magnetic Resonance) serves to detemine the molecular structure of pure solved compunds. NMR is a first class candidate for digital superresolution (DS), since the kernel is allways well known and independant of frequency.
Most important applications of DS in nmr are these:
Grey solid: Superresolution
Green line: Kernel
The effecf of DS in nmr is comparable to using duplicate frequency or field.
In addition, the long wings of the lorentzians are concentrated into a more gaussian profile of each peak, wich greatly simplifies area estimation by simple integration.
The example shows, how DS identifies a weak pentett, wich is confused by a strong doublett.
Note the correct identification of the minute peak at digit 125.
|Peak identification in noisy
Up to a noise level of 10%, superresolution is by far the best method to interprete even totally unclear data.
A short look on a superresolved spectrum shows very clear data, ready for direct and correct intuitive interpretation.
Working with complex patterns is much less time consuming thereby.
|Reducing sampling intervals
Spectrum A was sampled in 400% of the sampling time of B.
Though in B noise is stronger by a factor 2, its superresolution is by far more informative than the non-resolved A.
For high throuput optimization DH thus allows for an approximate duplicate sample flow per time.
Superresolution requires robust
In PROANALYSI::PEAKS, you find baseline-routines optimized especially for NMR.
The complete sequence of data analysis can be automatted and perfomed by simply pressing a button.