|Felix Macro Generator Command Help|
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Often the tail end of an fid does not equal zero because the entire fid is either shifted up or down slightly from the zero point. This is referred to as a DC offset. If an fid with a DC offset is fourier transformed a spike at zero frequency will appear. Worse, if the fid is zero-filled, the appended zeros will not extend from the last point for the fid but rather will be offset from the last point. This will be interpreted as a truncation artifact when performing a fourier transformation and cause wiggles at the base of peaks. Both of these adverse effects can be removed by simply adjusting the fid up or down so that the center of the fid is near zero. This feature is automatically added in the acquisition dimension when using the macro generator. If this is an option that you would like to control please let me know.
Linear prediction should only be used when the signal you are trying to predict has not decayed completely away to zero. If the signal has already decayed to zero then linear predicting further data points will generally add noise and not improve resolution. It is therefore best to always process data without any linear prediction and then compare the spectra to one with linear prediction. It is also best to try different linear prediction parameters and compare them to see which works the best. Remember processing parameters can have huge effects on the quality of the data.
For experiments that have dimensions that were collected with constant time evolution it is generally best to use mirror-image linear prediction. See the readme file of the pulse sequence or ask me if you are unsure if any of the dimensions were collected with linear prediction. In general the mirror-image linear prediction algorithm will give superior results and is much faster to perform.
Linear prediction works best when the signal is strong, truncated, and there are as few peaks as possible to predict. Because of this last feature it is best in 3-dimensional data sets where both the f1 (D2) and f2 (D3) are to be linear predicted to fourier transform the acquisition dimension (D1) first, then transform the f1 (D2) dimension without linear prediction and then process the f2 (D3) dimension with linear prediction. Afterwards the f1 (D2) dimension can be inverse fourier transformed, linear predicted, and then re-transformed. All of this is built into the macro generator and takes no extra work on your part.
The macro generator has a button that allows each of the window functions to be viewed. It is a good idea that you always view the function that you are using to make sure you know what you are doing to your data. This is especially true of gaussian functions where minor adjustments of the parameters can lead to huge changes in the shape of the function. No single setting when processing the NMR data will have a greater effect on the quality of your spectra than window functions. It is therefore important that you try several different functions to find the one that gives the best possible results.