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:
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