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:

Application Black line: Original data
Grey solid: Superresolution
Green line: Kernel
1H, 13C, 31P, 29Si, etc

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 data

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 baseline correction.
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.