2D Linear Prediction

2D Forward Linear Prediction adds data points with ampltitude to the end of the FID. It is similar to Zero-Fill, but adding points with amplitude can lead to sharper resonances especially if the FID was truncated due to too short of an acquisition time- the nuclei were not fully relaxed at the end of the FID. The linear prediction is accomplished by predicting what the amplitude of the points should have been after the last real data point acquired.

It can be accomplished in the direct detect dimension (F2/t2) and the indirect detect dimension (F1/t1). Normally sufficient data points are acquired in F2 that linear prediction is not used, but it is important in F1 as the amount of acquired data points is low- when acquiring 2D data, the points in F2 (np) do not really change the length of data acquisition, but the points in F1 (ni) are linearly correlated with the length of the experiment (so double ni, double the length of the experiment).

The Linear Prediction menu is located under the Processing tab then Zero-Filling/LP. Zero-Filling is discussed here:
2D Zero-Fill

1) To accomplish 2D Linear Prediction, first choose the dimension to zero-fill. This is under Processing Menu, then Set Processing Dimension. Normally, choose F1.

2) To accomplish Forward Linear Prediction, click on LP Filling, then select Forward in the box below the Zero-Fill.

3) In the Linear Prediction box on the lower right, when you click on LP Filling, the software should choose the From, and To and the Method as well as the Basis Points and the Coefficients. The From is where to start predicting points, the To is the amount of points to predict to, the Method is the way the prediction is accomplished, the Basis points are the amount of points being used, and the Coefficients are the amount of coefficients being used for the prediction.

From = usually the prediction is started at the last acquired data point (Original).

To = the amount of points to predict to, normally 2 times to 4 times the amount of points acquired (Original). The software may extend this number past 4X Original to the Zero-Fill number, but it is suggested to not exceed about 4X the Original points, and just zero-fill the rest of the points.

Method = Typically Zhu-Bax is best, but there are other options.

Basis Points = Amount of points acquired used to predict the points. Normally, this is all acquired points (Original). If you aborted the acquisition before the end, this number may be less than the Original (so ni = 200 but you aborted half-way through the experiment, then Basis Points = 100)

Coefficients = Typically the default value is fine.

4) The Spectrum Size option in the upper right of the window needs to be at least equal to the To points in the lower right. Any number of points in Spectrum Size that is greater the To points is zero-fill; however, if the Spectrum Size were smaller, then the linear predicted points will be ignored.

5) To see the effect of Linear Prediction, zoom in on 2 resonances close in shift and observe the effect with the change of zero-fill. However, until the Apodization/Weighting Functions are updated, the full effect of Linear Prediction will not be observed. Also, note processing/redraw speed of the computer. If it is too slow, you might decrease the predicted points and/or zero-fill.