Glenn Facey’s blog

In case you’re interested in experimental aspects of NMR, do not miss Glenn Facey’s blog at University of Ottawa. Whether you are an NMR facility manager or a scientist using NMR routinely, I’m sure you will find it a very useful resource.

http://u-of-o-nmr-facility.blogspot.com/

Introducing 2D Resolution Booster ™ (RB)

In recent entries I presented Resolution Booster as a simple but robust method to obtain highly resolved NMR spectra and showed some of its properties. Today I want to show the preliminary results we are getting by applying an extension of this method to 2D NMR spectra.

In order to illustrate the performance of the algorithm I have simulated a 2D spectrum of an A2B3 spin system with the following parameters:

• Spectrometer Frequency = 500 MHz
• Shift A = 4 ppm (2000 Hz)
• Shift B = 8 ppm (4000 Hz)
• JAB = 30 Hz
• Line Width = 30 Hz
• Data points = 2048 x 2048

As the coupling constant is very close to the line width (they are actually exactly the same, 30 Hz), the multiplets are not resolved (2D spectrum at the left). After applying 2D RB, the spectrum achieved has a higher resolution along both dimensions, where all multiplets are now clearly well resolved.

We are still working on this method but the results we are currently getting are certainly very promising and we are confident that it will soon become a very valuable tool for automated 2D NMR processing. It is not available in the current version of Mnova but it will be included in the new release scheduled for the end of January 2008. Together with my friend Stan Sykora, we will be presenting a poster on RB in ENC 2008 at Asilomar. Should you be attending ENC, please stop by to see us. We will be delighted to discuss this (or any other) topic with you.

Automatic Processing & SNR

Manuel Perez brought to my attention a possible drawback of the automatic processing scheme for 13C NMR spectra I proposed in my previous post. Basically, his main concern was that small peaks in spectra with low SNR could get suppressed when this procedure is applied.
Of course, he is absolutely right if the method is carried out exactly as it has been described in my post. The problem is that I believe my post was somewhat misleading in the sense that it stated that the weighting functions to be applied should be just a linear ramp combined with a cosine bell function. Whilst this is correct, it’s not enough!. One should not forget that, usually, 13C NMR spectra are weighted with exponential functions in order to improve sensitivity, in particular when the SNR is not very good (as it often occurs). When such a function has to be used, it should also be applied in the automatic processing method I have proposed! Do not forget that a sine-like apodization function does not have the same sensitivity enhancement power as an exponential function does.
Because of the linear ramp employed, the sensitivity of the f-domain spectrum gets poorer and the cosine bell (or 90º shifted sine bell) function is introduced in order to somehow compensate for the decrease of the SNR caused by the linear ramp function. However, this does not mean that an exponential function must not be applied to further increase the SNR as it would be the case of routine 13C NMR processing.So, for example, when using Mnova, one should activate the following weighting functions:


It is important to note that merging of several apodization functions in this way it’s possible because weighting is a linear operation (as is the convolution process).

Let’s take a real-life example which will illustrate some of the points I’ve been talking about in the last two posts. In the figure below I show a 13C spectrum:

We can appreciate a very bad baseline and the intense solvent (Methanol-D4) peaks. For convenience, I will first get rid of the solvent lines by means of the cutting tool available in Mnova (note that this is just a visual tool, the peaks are not physically removed from the spectrum).


Baseline correction could seem quite tricky in this spectrum but it’s not. A polynomial baseline correction with an order higher than 4 or the Whittaker Smoother method included in Mnova will do the job very efficiently as it’s depicted below

Other operations that have been applied to this spectrum were (1) exponential weighting of 1 Hz and (2) phase correction. This is just the standard way to process this kind of spectra.
Now I will apply the ‘automatic’ method. First I will apply the linear ramp and cosine bell weighting function (excluding the exponential one) just to show the issue raised by Manuel. Remember the processing requirements:

1. Apodization: Linear Ramp + Sine Bell 90º
2. Magnitude calculation after FT

This is the resulting spectrum. It’s evident that the SNR has decreased significantly and several peaks get suppressed. The point to remember here is that the exponential weighting function has been excluded.


Let me introduce the exponential function again (in combination with the linear ramp and Sine Bell 90º functions) but this time I will use a line broadening value of 3 Hz. Take a look at the new spectrum stacked on top of the spectrum processed with the standard method:


Now the SNR is comparable with the ‘normal’ spectrum and no peaks are missing, whereas the resolution of both spectra is very similar.

I hope that things are clearer now. Should anyone out there find any other problem with this method or just want to give his feedback about it, I will be more than happy to respond.

Automatic Processing of 13C NMR spectra

The days in which chemists had a lot of time to spend in the processing and evaluation of their NMR spectra has probably gone. Synthetic or medicinal chemists should use their precious time working on their lab benches whilst NMR spectroscopists are usually devoted to get the most from the NMR instruments (optimizing or designing new pulse sequences) and resolving the most challenging cases in structure elucidation/verification. Thus, in my opinion, any method permitting to automate processing steps would be very useful in accelerating spectral analysis in the framework of NMR structure determination.

Here I would like to introduce a very simple processing scheme which can greatly simplify the automatic processing of 13C NMR spectra. For the time being I will simply outline the operations required but I will leave for a future blog entry an explanation on how the method actually works under the hood.

The method starts by first multiplying the FID by a Cosine Bell function (the squared version would also work) combined with a 45º linear ramp function:

Next, we will apply the Fourier Transform followed by the magnitude calculation of the resulting frequency domain spectrum. That’s it! The resulting spectrum will exhibit the same resolution as a standard phase corrected spectrum despite of being in magnitude mode!. As I wrote, I will explain why this is so in this blog shortly

So, the advantages of this method are that neither phase nor baseline correction are required. It is true that, in the last years, automatic algorithms for phase and baseline corrections have become very reliable, but they are not bullet proof and sometimes they require manual tuning in order to get optimal results. For example, in the figure below you can see (top) a 13C spectrum with a severe baseline roll caused by the corruption of the first points of the FID. Whilst backward linear prediction or efficient baseline correction algorithms (see this) could be used to resolve this problem, the method I presented here will yield a very good spectrum with no user intervention at all (bottom).


That is all for now. I will soon answer some questions such as why these window functions are used and why phase and baseline correction are not needed but in the meantime, should you need any clarification, just drop me an email or leave a comment here.