One of the most ubiquitous issues present in FT-NMR spectra is the existence of baseline artifacts which might adversely affect the identification and quantification of NMR resonances. Whilst modern NMR instruments are equipped with powerful digital filtering employing also oversampling techniques that produce high quality baselines, it is usually the case that some minor baseline corrections might be needed in order to get optimal results. Also, it should not be forgotten that there are thousands of old NMR instruments lacking those latest instrumental advances where the necessity of a post-processing baseline correction might be critical.
Many baseline correction algorithms have been published since the very early era of FT-NMR, ranging from manual to fully automatic methods. Some of them have been implemented first in MestReC and then in Mnova. Whilst the automatic methods give quite satisfactory results in most of the cases, there are spectra in which a manual procedure could be more convenient.
Former versions of Mnova included the so-called ‘Multipoint Baseline Correction’ in which the User had to identify the points corresponding to baseline regions (also known as control points) which are then used by the software to build a baseline model using different interpolation algorithms (linear segments, polynomials, splines, etc).
Unfortunately, this manual method was not as robust as we initially thought and the process of selecting the control points was fully manual.
We thought that it would be very useful to implement a quick button to automatically detect these control points so that the User would only need to review them and if need be, edit or add a few more in order to get the optimal baseline.
This is exactly what is available now in version 9 of Mnova NMR: This new button runs a novel algorithm that analyzes all the points in a spectrum which is further split in different spectral windows. As a result of this process, a number of control points are automatically added to the spectrum.
Once all the control points are available, this module offers several possibilities to create the final baseline model: Whittaker, linear segments, smoothed linear segments, polynomials and splines. Of these, we recommend the cubic splines, they usually give very good results provided there are a sufficient number of control points well spread across the spectral width.
Automating the new algorithm
After having implemented this algorithm, we found that it would make sense to fully automate it and add it to our set of automatic baseline correction algorithms, both for 1D and 2D. It works as simple as this: First the algorithm detects automatically all the control points using the same method that has just been mentioned. Next, the baseline distortion is modeled using splines that go through all those control points.
This new algorithm is available from the baseline correction command:
These are just some examples: