
T1 Noise
Reducing Apparent T1noise in your processed 2D data.
User often ask how to reduce apparent T1 noise in their
2D data. Many aspects of 2D processing can contribute to
what may appear to be T1 noise. The first step is to try
to determine as much as possible what is creating the
problem. To do this you need to look at the transformed
data after the D1 transform and before the D2 transform.
Try loading some sample columns and saving these to
files. Try processing these and look for anything that
may produce a T1 ridge in the transformed data.
Baseline, phasing and appodization problems can all
cause these sorts of problems.
Anything that causes a distortion in the baseline can
produce artifacts that mimic T1 noise. Baseline
artifacts cannot always be removed entirely by baseline
correction. Therefore it is best to remove anything that
contributes to baseline distortion as early in the
processing cycle as possible. Many spectrometers tend to
mess up the acquisition of the first few points. Since
the first few points are critical for the baseline
anything that can be done to correct these points will
generally improve the baseline and hence the look of the
entire spectrum. For this reason many people tend to use
the lpf command to correct the intensity of the first
points in D1 and sometimes D2. Linear prediction is used
to estimate the value of the first few incorrectly
acquired points using the values of subsequent points.
Some people find that muliplying the first point by 0.5
works very well.
Often errors in phasing can mimic T1 noise. It is
therefore critical that the phase be determined as
accurately as possible. The rephase macro can be used to
correct the phase of an already processed 2D spectrum
very easily.
Apodization is also very important as it determines the
extent to which the tails of peaks extend from the
center. Felix provides many types of apodization
functions including sinebell, sinebell squared, skewed
sinebell, skewed sinebell squared, exponential
linebroadening, gaussian linebroadening, kaiser and
trapezoidal functions. Try various apodization functions
until the data looks good and there is not much tailing
of peaks. Remember that if you process your data as
States then you would normally apodize over a number of
points equal to onehalf the total number of acquired
fids. This is because the data is complex in D2 and a
complex vector contains half as many complex data points
as the corresponding real vector did. Incorrect
apodization in D2 is probably the most common cause of
ridges along D2.
After you have decided on the appropriate processing
parameters for the second dimension then go ahead and
process D2. Then take a look at the phasing and baseline
for the columns. If you need to rephase or correct the
baseline in the D2 dimension you can do this after the
dimension has been processed by using the appropriate
commands under the Process pulldown.


• Back
to FAQ's
• Contact
us
